The University of Manchester implements the Tanium XEM platform, enhancing cybersecurity for 40,000 endpoints after a significant data breach.

University of Manchester Enhances Cybersecurity With Tanium - SDxCentral

As Cybersecurity Awareness Month nears, leaders from Tanium, Diligent, and AvePoint advocate for automation, improved governance, and data management to combat escalating cyber threats.

Automation key to reducing cybersecurity burnout & risks

Veteran channel chief Todd Palmer has made the jump to cybersecurity vendor Illumio from Tanium, amid what he described as a massive growth opportunity for partners in zero-trust security.

Illumio Hires Veteran Of Tanium, Palo Alto Networks As Channel Chief

Millions have been lost to scammers and cases are still rising in Singapore. What can you do to avoid falling prey? And if that is too late, what should you do to minimise your losses?

FAQ: How to avoid being scammed and what to do if you become a victim

Cybersecurity practitioners are expressing concern after the U.S. Securities and Exchange Commission sued both SolarWinds Corp. and its Chief Information Security Officer Timothy Brown for fraud relating to the hack of the company first disclosed in December 2020.

Cybersecurity practitioners fret after SEC sues SolarWinds and its CISO

Hanah-Marie Darley, director of threat research at cybersecurity firm Darktrace, told The Independent HMRC was at “increased risk” from hackers because it holds “very sensitive data”.

UK at risk of massive security breach from national IT meltdown

Singaporean cyber risk management firm Protos Labs has raised SGD$3 million (approximately $2.2 million USD) in seed funding from BEENEXT, VinaCapital Ventures, Artem Ventures (in partnership with FWD Insurance), Plug and Play Silicon Valley, Investible, Gan Konsulindo, 1337 Ventures, and Gobi Partners.

RTX (formerly known as Raytheon) is selling its cybersecurity business. Accenture acquires MNEMO Mexico. CISO challenges, across sectors.

The FBI warns that cybercriminals are targeting plastic surgery offices and stealing sensitive information, including medical records, in a multi-stage cyber extortion campaign.

FBI Warns About Cyber Extortion Scheme Targeting Plastic Surgery Facilities and Customers

Get the latest federal technology news delivered to your inbox. The National Institute of Standards and Technology is revising segments of its security controls, part of a larger patch release ...

NIST to issue cyber updates, introduce new security controls

It is "National Cybersecurity Awareness Month," and as new threats emerge daily, cybersecurity experts are offering new tips beyond just changing your password. Vivek Bhandari from Tanium joins Jim on "The Final 5.

How to better securing your online presence

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Handling Smart Contracts with Ioana Frincu on The HackerNoon Podcast

There’s an ever-increasing reliance on technology in today’s workplace. That’s good news for IT professionals, whose skills are in great demand and whose work can often be done remotely. According to the Bureau of Labor Statistics, . This is a much faster rate than the average for all occupations. About 531,200 new jobs are projected to be added during this time frame. computer and information technology jobs are projected to grow 11% between 2019 and 2029 IT jobs are typically remote-friendly, too. And, if is any indication, remote work isn’t going anywhere. When asked, 65% of respondents said they want to keep working remotely full-time when the pandemic is over. our recent survey We’ve compiled a list of 20 companies that commonly hire for work-from-home IT jobs, along with some recent job postings. Check out these remote IT job opportunities, and if you’re a FlexJobs member, log in to your account and to see the full postings. Note: FlexJobs is a subscription service for job seekers that features flexible and remote jobs. With an A+ rating from the Better Business Bureau , the monthly subscription costs allow us to fully vet and verify all of the jobs on our site —ensuring that customers have a safe and positive job searching experience. Companies That Hire for the Most Remote IT Jobs 1. Cisco A global technology solutions firm, Cisco has become a powerhouse of internet and communications products, services, and solutions that have revolutionized the way business is conducted. Recent remote IT jobs: Senior Systems Engineer Senior Infrastructure Operations Engineer – Compute Senior IP Network Technical Lead 2. Cognizant Softvision A division of Cognizant, Softvision’s goal is to continuously improve its clients’ digital maturity, combining skill, scale, and cost in each stage of the product development lifecycle to create unforgettable digital experiences. Recent remote IT jobs: Solution Architect Mobile Device Management Engineer 3. ConsenSys A blockchain venture production studio based in Brooklyn, New York, ConsenSys builds and scales consumer-centric enterprise software products and tools powered by Ethereum, a public, blockchain-based, open-source distributed computing platform and operating system featuring smart contract functionality. Recent remote IT jobs: Technical Support Engineer, Multi-Lingual Technical Support Engineer Help Desk Agent 4. CrowdStrike CrowdStrike provides cloud-delivered security technology, threat intelligence, and endpoint protection for blue-chip companies in multiple industries. Recent remote IT jobs: Escalation Engineer – Services Engineering Red Team Tool Developer Security Analyst 5. Fastly Internet company Fastly provides the Fastly Edge Cloud platform, a content delivery network that provides clients with improved web and application performance, quality video and streaming, and cloud security. Recent remote IT jobs: Senior Director, Hardware Systems Engineering Senior Provisioning Engineer Senior IT Systems Engineer 6. Gartner Gartner, an information technology research and advisory company, delivers technology-based insights to help companies and organizations around the world make efficient, high-quality business decisions. Recent remote IT jobs: Consulting Associate Director, IT Strategy, Data and Analytics IT Services and Software Analyst Security Programs Director 7. General Dynamics General Dynamics is a global aerospace and defense company. The company has businesses in aerospace, combat systems, information technology, marine systems, and mission systems. Recent remote IT jobs: Dynamics 365 Operation and Maintenance Engineer Senior Appian Developer – DevSecOps 8. GitHub GitHub is an open-source code-hosting website, publishing service, and social networking site for programmers. It has hosted more than 66 million projects and supports a community of over 24 million people. Recent remote IT jobs: Staff Product Security Engineer Staff Security Engineer – Operations and Telemetry Business Systems Analyst, People Systems 9. Kelly Kelly has provided workforce and staffing solutions since 1946. They place workers in diverse industries, including information technology, life sciences, call centers, and more. The company also provides outsourcing and consulting services. Recent remote IT jobs: IT Project Manager – Healthcare Database Architect IT Technical Writer 10. Kforce Kforce is a publicly-traded staffing and recruiting firm that provides staffing solutions in the technology, financial services, healthcare, communications, and government sectors. Recent remote IT jobs: Cloud Operations Engineer Desktop Support Technician Technology Manager – Database 11. Motion Recruitment Partners The parent company of three recruitment agencies, Jobspring Partners, Sevenstep, and Workbridge Associates, Motion Recruitment Partners places people in computer & IT, software development, writing and editing, and graphic design jobs. Recent remote IT jobs: Senior Security Engineer Lead Systems Administrator 12. NTT Group NTT Group consists of regional, long-distance, international, mobile, and data communications businesses. Their mission is to provide high-quality, value-added services that enrich their clients in the areas of artificial intelligence, big data, and the internet-of-things. Recent remote IT jobs: Incident Manager COBOL Developer Data Backup-System Integration Specialist 13. Rackspace Rackspace Technology is a leading managed cloud company dedicated to helping businesses harness the power of cloud computing without the cost and complexity of having to manage it on their own. Recent remote IT jobs: Cloud Architect – Rackspace Government Solutions ServiceNow Business Analyst ServiceNow Architect 14. Randstad One of the biggest staffing firms in the U.S., Randstad provides HR solutions to clients in the accounting, engineering, IT, human resources, and logistics sectors. Recent remote IT jobs: IT Project Manager Informatica Developer Systems Analyst 15. Red Hat Red Hat is an award-winning technology firm. The company develops a broad range of solutions and provides services that are not only open and secure but trusted by over 90% of the Fortune Global 500. Recent remote IT jobs: Software Maintenance Engineer Senior Business Systems Analyst Senior Solution Manager – Senior Product Manager 16. Robert Half International Robert Half is a staffing and consulting firm that places job seekers in a variety of career fields, including computer & IT, administrative, and accounting and finance. The company consists of eight divisions and has over 400 global locations. Recent remote IT jobs: Sharepoint Developer Database Administrator Developer Network Manager 17. Tanium Tanium products help companies with IT security and systems management. Their platform offers 15-second visibility and control of all endpoints on some of the most complex and largest networks in the world. Recent remote IT jobs: Senior Manager, Web Operations Senior Director, Product Management – Security Operations Senior QA Engineer 18. Twilio Twilio is a Platform-as-a-Service (PaaS) company operating as a cloud communications company. Users can make and receive calls using texts and web service APIs. The company also provides clients with privacy and security for messaging. Recent remote IT jobs: Senior Information Security Compliance Analyst Technical Onboarding Manager 19. Unisys Using secure, reliable, advanced technology, Unisys provides clients in the commercial, government, and financial services industries with solutions to enhance the lives of their customers and citizens. Recent remote IT jobs: Enterprise Architect Storage Administrator Cloud Security Engineer 20. UnitedHealth Group UnitedHealth Group consists of two businesses: UnitedHealthcare (a benefits and insurance provider) and Optum (a provider of technology and information-enabled health services). The company provides benefit services and healthcare coverage in all 50 states and in 125 countries worldwide. Recent remote IT jobs: Server Administrator Senior IT Project Manager Senior Director, Quality Operations and Analytics Landing a Work-from-Home IT Job With FlexJobs FlexJobs has a whole category dedicated to , with six subcategories: flexible computer and IT jobs Computer Security Database Administration Networking SQL System Administration Technical Support All of our job listings are fully vetted and verified, giving you a safer, faster, and better job search experience. Previously published at https://www.flexjobs.com/blog/post/top-companies-hiring-for-remote-it-jobs-v2/

20 Companies With Remote Tech Jobs

FlexJobs just completed it's and companies dominated this year’s list! Through an analysis of the remote job posting histories of approximately 57,000 companies in the FlexJobs database, we’ve pinpointed the companies that offered the most remote jobs from January through December 2020. 8th annual list of the Top 100 Companies to Watch for Remote Jobs Computer & IT Below are the 22 tech companies that are known for hiring remote workers, as well as their respective rank on FlexJobs’ list. Software Engineer, Web Developer, Project Manager, and IT Consultant are also among the most common job titles for remote jobs! 13 	Citrix 23 	Oracle 25 	Salesforce 36 	VIQ Solutions 38 	Twilio 41 	Dell Technologies 42 	CrowdStrike 45 	Veeva 46 	SAP 47 	Red Hat 49 	Shopify 50 	Atlassian 54 	NTT Group: NTT DATA 57 	GitHub 59 	Keywords Studios 60 	Elastic 64 	General Dynamics; General Dynamics Information Technology 69 	Motorola Solutions 70 	Tanium 73 	Pega - Pegasystems, Inc. 81 	ServiceNow 83 	Cisco 92 	Leidos To view the entire 2021 Top 100 Companies for Remote Jobs list, please visit . https://www.flexjobs.com/blog/post/100-top-companies-with-remote-jobs-2021/

22 Top Tech Companies Hiring Remote Workers in 2021

DevSecOps is the theory of incorporating security activities within the process of DevOps. Organizations support DevOps because it puts the fields of growth and operations closer together. This model of production provides a responsive and reliable way of continually delivering code to the real world while retaining necessary checks and balances. One way to fix this vulnerability is the DevSecOps strategy, which guarantees that the security liability is not left to one or two participants. Instead, all aspects of the process are woven into security, depending on shared responsibility from production through operations, test automation, efficient communication and other techniques. What is DevSecOps? The theory of incorporating security activities within the process of DevOps is DevSecOps. It generates a new model called DevSecOps when protection is introduced into the . DevOps process DevSecOps includes thought from the outset about the protection of the application and infrastructure. To sustain the DevOps strategy, it also includes automating individual security gates. The Benefits of DevSecOps: Greater capability for automation Lowered chance of errors Reduced Manual Security Setup Requirement At an earlier stage in the process, more protection Protection that is designed explicitly for the product, not an add-on Let's discuss the in order to handle security effectively in the . 7 DevSecOps best practices DevOps model 1. Automating security The DevOps system is characterized by shared accountability, and the word DevSecOps applies the same attitude to security issues. Leverage the power of automation to make security testing easier, safer, and less disruptive. For security testing and analysis in a DevSecOps sense, various test automation tools are available. From source code analysis to post-deployment and testing of integration, they do everything. Splunk, Metasploit, Tanium, Sonatype, Contrast Security are among them. 2. Using the right tools Don't presume that you will be handling protection with current DevOps software. Many either have no safety features themselves, or they concentrate solely on the protection of the workflows between workers in production and operations. Look for tools that can verify the security of code against established problems and, preferably, zero-day issues in real-time to adhere to DevSecOps best practices. Look also for capabilities for pen testing; these allow you to largely automate security testing. 3. Encrypt security between services with DevSecOps A microservice-based architecture between the different services involved would rely on (APIs). The security of the APIs, both in how the development team writes the service code and in the external services that are called, must be maintained and constantly checked. application programming interfaces In any service code, developers should have sufficient protection. The team of ops must ensure that APIs are tracked to maintain safe communications at all times. 4. Educate the developers Security team members are also seen by developers as a roadblock, something that slows them down and prevents them from meeting deadlines. They gain a deeper understanding of why security is an essential part of the development cycle when you on and why they are relevant. educate your developers secure coding practices 5. Ensure protection between the device and the back-end While the emphasis in DevSecOps should be on the production and consumption of data, be sure to see how the access system and its user access the back-end services. Simple protection problems and solution approaches are no longer significant enough for general use. Do the following instead: Implementing (MFA). multi-factor authentication Using encryption for streams of data. Tie the identity of the user into a granular directory system based not only on the person's name and location but also on where they are, the type of connexion used and the degree of access to the data of specified significance. 6. Use DevSecOps to put your request through security controls Daily checking should be carried out on your submission. More rigorous research, such as stopping denial of service attacks, should also be performed. In a solution, there can be bugs that are only apparent when that solution is broken. There are also real concerns that could be faced by the product owner. 7. Scale and track Regardless of how many development teams you have working on different projects, the DevSecOps methodology needs to be flexible around the enterprise. Once you stick to the best practices mentioned above, scaling is simplified. Automated testing, collaboration, and the use of DevSecOps pipeline management optimizing tools make application protection a repeatable and scalable process that can be implemented through the entire enterprise into a dedicated DevSecOps approach. Conclusion I hope this short guide helped you understand just what DevSecOps means and the benefits of it. The technique of DevSecOps has gained traction because of the high cost of fixing security problems and secured debt. Security monitoring becomes more critical as Agile teams release applications more regularly. We hope that some of the best practices listed in this article will assist your business to switch from DevOps to a DevSecOps strategy.

What is DevSecOps? - 7 Best Practices for Effective Automation in 2021

It’s been an exciting, but complex year in the data world. Just as , the data tech ecosystem has continued to “fire on all cylinders”. If nothing else, data is probably even more front and center in 2018, in both business and personal conversations. Some of the reasons, however, have changed. last year On the one hand, data technologies (Big Data, data science, machine learning, AI) continue their march forward, becoming ever more efficient, and also more widely adopted in businesses around the world. It is no accident that one of the key themes in the corporate world in 2018 so far has been “ ”. The term may feel quaint to some (“isn’t that what’s been happening for the last 25 years?”), but it reflects that many of the more traditional industries and companies are now fully engaged into their journey to become truly data-driven. digital transformation On the other hand, a much broader cross-section of the public has become aware of the pitfalls of data. Whether it is through the very public debate over the , the Cambridge Analytica scandal, the massive Equifax data breach, GDPR-related privacy discussions or reports of growing , the data world has started revealing some risks of AI government surveillance in China darker, scarier undertones. Both are the flipside of the same phenomenon, which has been brewing for many years but is now in full display: just about everything (whether personal or professional) is rapidly getting digitized, and data technologies are becoming more adept than ever at processing and analyzing this massive data exhaust, increasingly in real time. From this can result . The debate on how to combine this great power with a necessary sense of responsibility has become essential. both magic and abuse Let’s highlight some of the key trends and events of 2018. Infrastructure & Analytics From an industry standpoint, the data ecosystem remains as exciting and vibrant as ever, with a rich tapestry of innovative startups, mature “scale-ups”, and many aggressive public technology vendors. Most importantly, many customers large and small are those technologies , and reaping undeniable value from their efforts. deploying in production at scale As the cycle of replacing older IT technologies with more modern data products continues, it seems that the Big Data market (infrastructure, analytics) is cycling through the early majority of buyers and transitioning into the late majority of the traditional adoption curve. In addition, the data world continues its inexorable evolution towards the cloud. It is actually staggering to see how fast the large public cloud providers (AWS, Azure, Google Cloud Platform, IBM) are growing, considering they already each generate billions of dollars of revenues every quarter. The trend raises ongoing concerns around , and this may open up opportunities for startups offering solutions. However, to date even companies adopting multi-cloud strategies tend to still rely on one vendor as their primary provider. vendor lock-in multi-cloud As they keep growing, large cloud providers increasingly compete with each other by offering a wide array of Big Data, data engineering and machine learning tools through their platforms (e.g., Amazon Neptune, Google AutoML, etc.) — and often with aggressive pricing, to attract more developers, as their true business model is data storage. As the scope and sophistication of such tools keep growing, this has a big impact on the data technology landscape, making it arguably , at least for broad, horizontal opportunities. A bit more every year, the list of product announcements at big annual cloud vendor conferences (see AWS , for example) sends shockwaves in the startup industry, as they put cloud vendors in direct competition with dozens of VC-backed startups in one fell swoop. It will be interesting to see how public markets react to the upcoming Elastic IPO, an open-source software company that saw Amazon launch a direct competitor, Elasticsearch, three years ago. harder for startups to compete re:Invent Plenty of opportunities for startups remain, however, as long as they are sufficiently differentiated. Many in the space are scaling fast, and there are a number of particularly interesting, fast-growing segments in the infrastructure and analytics part of the ecosystem, including /real-time, , and . The explosion of interest in AI has also led to great opportunities (and a lot of funding) in , , tools, and platforms enabling the streaming data governance data fabrics/virtualization AI chips GPU databases AI devops deployment of data science and machine learning in the enterprise. Machine Learning & AI It’s certainly been a , with anything from the prowess of AlphaZero to the staggering pace of release of new advances — new forms of Generative Adversarial Networks, Vicarious’ new Recursive Cortical Networks, Geoff Hinton’s new Capsule Networks. AI conferences like NIPS have grown to attract 8,000 people and thousands of academic papers are being submitted every day. wild year in the world of AI research At the same time, the pursuit of , perhaps thankfully so. Much of the current wave of excitement (and fear) about AI results from the impressive performance of deep learning since 2012 but, in the AI research community, there’s a as some question the foundations of deep learning (backpropagation) and others look to move past what they consider “brute force” approaches (lots of data, lots of computing power), perhaps in favor of more neuroscience-based approaches. AGI remains elusive growing sense of “what now?” Far from fearing robot world domination, many in the AI research community are concerned that continued over-hyping of the field may eventually disappoint and lead to another AI nuclear winter. Outside of AI research, however, we are just at the beginning of a wave of deployment and application of deep learning in the real world across a variety of problems involving speech recognition, image classification, object recognition and language, in different industries. If the infrastructure and analytics part of the ecosystem is getting to the late majority, we’re still very much in early adopter territory for enterprise and vertical AI applications. The that started a year or two ago has mostly continued unabated, even though the AI startup market is (arguably) showing signs of finally cooling down. Expectations, round sizes and valuations remain high, but we are certainly past the phase where big Internet companies would snap up very early AI startups at high prices just for the talent. The air is also clearing up a bit and revealing “real” AI startups, versus a number of other companies that were leveraging the hype. Some of the AI startups that were founded in the 2014–2016 time frame are starting to hit early scale, and many are offering increasingly interesting products including health, finance, “industry 4.0” and back office automation. Deep learning will continue bringing a lot of value in real world applications for years to come, and vertical-focused AI startups have many great opportunities ahead of them. Cambrian explosion of deep-learning based startups across industries and verticals This continued explosion is very much a global phenomenon, with Canada, France, Germany, the U.K. and Israel being particularly active. However, , with reports of government-led pooling of data at mind-boggling scale (across Internet companies and municipalities), rapid advances in areas such as facial recognition and AI chips, and gigantic rounds of financing for its startups: according to CB Insights, China accounted for only 9% of global AI deal share but nearly 48% of global AI funding in 2017, up from 11% in 2016 (see some examples below). China seems to be playing at a completely different level in AI In the same vein, (and ownership and security) are emerging as a major concern around the world. In the early days of the Internet, data privacy was about protecting what we did online, a comparatively small portion of our activities. Correspondingly, only a small (albeit vocal and passionate) minority of people truly cared. As just about every aspect of our personal and professional lives is now connected to the Internet through an ever increasing array of connected devices, the stakes are changing. With its ability to spot anomalies in massive data sets, predict outcomes and recognize faces, issues of data privacy AI is compounding the data privacy problem. A separate but related concern is that a lot of this data is owned by large Internet companies (GAFA). Some, like Facebook, have proven to be a less than perfect steward of it. Nonetheless, this data provides them an unfair advantage in the race to produce ever more powerful AI. Against those issues, an emerging theme is to think of the , as well as a way for others, outside of GAFA, to produce great AI. Crypto economics are viewed as a way to incentivize individuals to provide their personal data and for machine learning engineers to build models by processing this data anonymously. It all remains very experimental, but some early marketplaces and networks are emerging blockchain as a possible foil against the risks of AI The 2018 Landscape Without further ado, here’s our 2018 landscape. Quite semantic note: buzz terms come and go. Fewer people speak about “Big Data”, many more about “AI”, often to described the same reality. Consequently, we have slightly rebranded our 2018 landscape: it is now called the “Big Data & AI” landscape! To see the landscape at full size, . click here (The image is high-res and should lend itself to zooming in well, including on mobile) To download the full-size image, . click here To view a full list of companies in spreadsheet format, . click here This year, my FirstMark colleague provided immense help with the landscape. Demi Obayomi We’ve detailed some of our methodology in the notes to this post. Thoughts and suggestions welcome — please use the comment section to this post. Who’s in, who’s out On the exit front, the last year (since our 2017 landscape) has seen solid, but not extraordinarily strong. A few key companies appearing on the landscape went public, in particular Cloudera, MongoDB Pivotal and Zuora. Others are preparing to go out at the time of this writing, such as Elastic. Some notable acquisitions also occurred, including in particular Mulesoft (acquired by Salesforce post-IPO, for $6.5B), Flatiron Health (acquired by Roched for $2.1B), Appnexus (acquired by AT&T for $1.6B), Syncsort and Vision Solutions (acquired for $1.2B by Centerbridge Partners), Moat (acquired by Oracle for $850M), Integral Ad Science (acquired by Vista Equity Partners for $850M), eVestment (acquired by NASDAQ for $705M) and Kensho (acquired by S&P Global for $550M). It is worth noting that, other than Mulesoft, all those companies are headquartered on the East Coast (New York, Boston and Atlanta). Many other companies were also acquired for smaller amounts: Gigya (SAP), Blue River Technology (Deere & Co), CoreOS (Red Hat), Guavus (Thales), Lattice Data (Apple), Socrata (Tyler Technologies) and PracticeFusion (AllScripts). On the investment front, this was a year of big financing rounds for some Big Data and AI startups, particularly in China, with a number of oversized investments including Bytedance ($3B in total across 2 rounds in 2017), NIO ($1.6B across two rounds in 2017), and SenseTime ($850M across two around in 2017 and 2018). Major rounds of US companies appearing on the landscape include Snowflake Computing ($263M Series A — see our recent at ), Cohesity ($250M Series D), Dataminr ($221M Series E), Affirm ($200M Series E), Rubrik ($180M Series D), Qualtrics ($180M Series C — see an older but still relevant at Data Driven NYC), Tanium ($180M private equity round), ThoughtSpot ($145M Series D) and Coveo ($100M private equity round) and C3IoT ($100M Series F). fireside chat Data Driven NYC fireside chat ____________________ NOTES: As every year, we couldn’t possibly fit all companies we wanted on the chart. While the general philosophy of the chart is to be as inclusive as possible, we ended up having to be somewhat selective. Our methodology is certainly imperfect, but in a nutshell, here are the main criteria: Everything being equal, we gave priority to companies that have reached some level of market significance. This is a reasonably easy exercise for large tech companies. For growing startups, considering the limited amounts of data available, we often used venture capital financings as a proxy for underlying market traction (again, probably imperfect). So everything else being equal, we tend to feature startups that have raised larger amounts, typically Series A and beyond. Occasionally, we made editorial decisions to include earlier stage startups when we thought they were particularly interesting. On the application front, we gave priority to companies that explicitly leverage Big Data, machine learning and AI as a key component or differentiator of their offering. As discussed in the piece, it is a tricky exercise at a time when companies are increasingly crafting their marketing around an AI message, but we did our best. This year as in previous years, we removed a number of companies. One key reason for removal is that the company was acquired, and not run by the acquirer as an independent company.. In some select cases, we left the acquired company as is in the chart when we felt that the brand would be preserved as a reasonably separate offering from that of the acquiring company. As always, it is inevitable that we inadvertently missed some great companies in the process of putting this chart together. Did we miss yours? Feel free to add thoughts and suggestions in the comments. The chart is in png format, which should preserve overall quality when zooming, including on mobile. As we get a lot of requests every year: feel free to use the chart in books, conferences, presentations, etc — two obvious asks: (i) do not alter/edit the chart and (ii) please provide clear attribution (Matt Turck, Demi Obayomi and FirstMark Capital). Disclaimer: I’m an investor through FirstMark in a number of companies mentioned on this Big Data Landscape, specifically: ActionIQ, Cockroach Labs, Dataiku, Frame.ai, Helium, HyperScience, Kinsa, Timber, Sense360 and x.ai. Other FirstMark portfolio companies mentioned on this chart include Bluecore, Engagio, HowGood, Payoff, Knewton, Insikt, Optimus Ride, and Tubular. I’m a small personal shareholder in Datadog.

Great Power, Great Responsibility: The 2018 Big Data & AI Landscape

Authors:
(1) Clayton W. Kosonocky, Department of Molecular Biosciences, The University of Texas at Austin (clayton.kosonocky@utexas.edu);
(2) Aaron L. Feller, Department of Molecular Biosciences, The University of Texas at Austin (aaron.feller@utexas.edu);
(3) Claus O. Wilke, Department of Integrative Biology, The University of Texas at Austin and Corresponding Author (wilke@austin.utexas.edu);
(4) Andrew D. Ellington, Department of Molecular Biosciences, The University of Texas at Austin (ellingtonlab@gmail.com). Table of Links Abstract & Introduction
Methods
Results and Discussion
Determining Whether Canonicalization Impacts Search Behavior
Explanation of Search Behavior & Drawbacks, Future Improvements, and Potential for Misuse
Conclusion, Acknowledgements, Author Contributions, & more.
Supplementary Figures ABSTRACT Chemical similarity searches are widely used in-silico methods for identifying new drug-like molecules. These methods have historically relied on structure-based comparisons to compute molecular similarity. Here, we use a chemical language model to create a vector-based chemical search. We extend implementations by creating a prompt engineering strategy that utilizes two different chemical string representation algorithms: one for the query and the other for the database. We explore this method by reviewing the search results from five drug-like query molecules (penicillin G, nirmatrelvir, zidovudine, lysergic acid diethylamide, and fentanyl) and three dye-like query molecules (acid blue 25, avobenzone, and 2-diphenylaminocarbazole). We find that this novel method identifies molecules that are functionally similar to the query, indicated by the associated patent literature, and that many of these molecules are structurally distinct from the query, making them unlikely to be found with traditional chemical similarity search methods. This method may aid in the discovery of novel structural classes of molecules that achieve target functionality. Keywords: Drug Discovery · Machine Learning · Chemical Similarity Search · Prompt Engineering · SMILES 1. Introduction Applications for small molecules in modern society are various and widespread, including treatment of heritable disease, pathogen inhibition, and the generation of functional materials for use in electronics and consumer goods. Molecular function emerges from structure, but it is not always obvious how this emerges from first principles due to the dependence of function on the target molecule [1]. Traditionally, exploration of natural products has led to the identification of vital pharmaceuticals and specialty chemicals [2–5]. These first generation molecules act as starting points, upon which new molecules are engineered for furthering desired functionality [6]. Structural neighbors often share similar functionality, as the relevant chemistry may be unchanged or improved [7]. However, molecules with low structural similarity can act on the same target despite the highly different structure, as is the case with morphine and fentanyl on the µ-opioid receptor [8]. There are numerous contemporary approaches to applying machine learning to chemistry [9–16]. However, the application of language models to this space has led to surprising success in predicting biochemical features such as drug-likeness and protein-ligand interactions [11, 13]. These methods require string representations of molecules, commonly using the Simplified Molecular-Input Line-Entry System (SMILES) [17]. Language models are often trained in an unsupervised manner with the reward function tied to sequence reconstruction, i.e. feeding the model a masked or partial input with the goal of reconstructing the original sequence. It was recently demonstrated that a chemical language model, though trained only on SMILES strings, correctly predicted complex biophysical and quantum-chemical properties [18]. This points to the possibility that these models develop a chemical latent space that allows for the emergence of higher-order biochemical comprehension. Recently, computationally generated chemical libraries have grown to surpass 37 billion commercially available compounds [19]. This marked growth has generated a new field of computational pre-screening of chemicals in order to support resource efficient discovery in the laboratory [20]. One primary class of computational pre-screening methods are chemical similarity searches. These methods have historically used structure-based comparisons, a notable example being the fingerprint Tanimoto search which computes a hierarchical list of molecules ranked by molecular substructure similarity to a given query [21, 22]. Chemical Language Models (CLM) have been applied to drug discovery, in particular de novo molecule generation and chemical similarity searches. De novo methods generate novel molecules with the decoder portion of a language model after fine-tuning toward a specific molecule or target [14–16, 18]. Building off the recent success of generative models such as GPT, de novo molecule generation has shown promise but is limited due to a lack of generalizability and guaranteed synthesizability [23, 24]. In contrast, a chemical similarity search based on a CLM has the advantage of computational speed, generalizability, and high database control to ensure synthesizability. Sellner et al. recently created a novel transformer-based chemical similarity search with an optimized loss function to approximate previous structure-based methods [12]. However, this method tends to identify molecules with high structural similarity to the query molecule, when instead we would often like to find structurally distinct functional analogues. Such a CLM-based search does not currently exist. Here, we describe a chemical similarity search based on a CLM that identifies molecules with similar function to a given query molecule. This method works by calculating the CLM-computed feature vector similarity between a query SMILES string and a chemical database. Keeping the SMILES canonicalization algorithm constant between the query and database resulted in a chemical language search that approximated recent transformer-based chemical search methods. However, we found that when the query SMILES string was canonicalized with a different algorithm than was used for the database, the reliance on structural similarity diminished while functional similarity was retained. This behavior seemed reasonable given the literature describing how models learn to better represent chemical space when SMILES randomization occurs during training and from the reported emergent understanding of underrepresented languages in predominantly English-trained models [25–28]. We utilize alternatively canonicalized queries as a novel prompt engineering strategy to identify structurally distinct functional analogues of small molecules. Our method fundamentally differs from existing literature in that SMILES augmentation was used for the query of a chemical similarity search rather than for model training and fine-tuning to a specific task. We tested our method across three canonicalizations and eight query molecules and found that with increasingly divergent canonicalizations we were able to identify structurally distinct functional analogues. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Clayton W. Kosonocky, Department of Molecular Biosciences, The University of Texas at Austin (clayton.kosonocky@utexas.edu); (2) Aaron L. Feller, Department of Molecular Biosciences, The University of Texas at Austin (aaron.feller@utexas.edu); (3) Claus O. Wilke, Department of Integrative Biology, The University of Texas at Austin and Corresponding Author (wilke@austin.utexas.edu); (4) Andrew D. Ellington, Department of Molecular Biosciences, The University of Texas at Austin (ellingtonlab@gmail.com). Authors: Authors: (1) Clayton W. Kosonocky, Department of Molecular Biosciences, The University of Texas at Austin (clayton.kosonocky@utexas.edu); (2) Aaron L. Feller, Department of Molecular Biosciences, The University of Texas at Austin (aaron.feller@utexas.edu); (3) Claus O. Wilke, Department of Integrative Biology, The University of Texas at Austin and Corresponding Author (wilke@austin.utexas.edu); (4) Andrew D. Ellington, Department of Molecular Biosciences, The University of Texas at Austin (ellingtonlab@gmail.com). Table of Links Abstract & Introduction Methods Results and Discussion Determining Whether Canonicalization Impacts Search Behavior Explanation of Search Behavior & Drawbacks, Future Improvements, and Potential for Misuse Conclusion, Acknowledgements, Author Contributions, & more. Supplementary Figures Abstract & Introduction Abstract & Introduction Methods Methods Results and Discussion Results and Discussion Determining Whether Canonicalization Impacts Search Behavior Determining Whether Canonicalization Impacts Search Behavior Explanation of Search Behavior & Drawbacks, Future Improvements, and Potential for Misuse Explanation of Search Behavior & Drawbacks, Future Improvements, and Potential for Misuse Conclusion, Acknowledgements, Author Contributions, & more. Conclusion, Acknowledgements, Author Contributions, & more. Supplementary Figures Supplementary Figures ABSTRACT Chemical similarity searches are widely used in-silico methods for identifying new drug-like molecules. These methods have historically relied on structure-based comparisons to compute molecular similarity. Here, we use a chemical language model to create a vector-based chemical search. We extend implementations by creating a prompt engineering strategy that utilizes two different chemical string representation algorithms: one for the query and the other for the database. We explore this method by reviewing the search results from five drug-like query molecules (penicillin G, nirmatrelvir, zidovudine, lysergic acid diethylamide, and fentanyl) and three dye-like query molecules (acid blue 25, avobenzone, and 2-diphenylaminocarbazole). We find that this novel method identifies molecules that are functionally similar to the query, indicated by the associated patent literature, and that many of these molecules are structurally distinct from the query, making them unlikely to be found with traditional chemical similarity search methods. This method may aid in the discovery of novel structural classes of molecules that achieve target functionality. Keywords : Drug Discovery · Machine Learning · Chemical Similarity Search · Prompt Engineering · SMILES Keywords 1. Introduction Applications for small molecules in modern society are various and widespread, including treatment of heritable disease, pathogen inhibition, and the generation of functional materials for use in electronics and consumer goods. Molecular function emerges from structure, but it is not always obvious how this emerges from first principles due to the dependence of function on the target molecule [1]. Traditionally, exploration of natural products has led to the identification of vital pharmaceuticals and specialty chemicals [2–5]. These first generation molecules act as starting points, upon which new molecules are engineered for furthering desired functionality [6]. Structural neighbors often share similar functionality, as the relevant chemistry may be unchanged or improved [7]. However, molecules with low structural similarity can act on the same target despite the highly different structure, as is the case with morphine and fentanyl on the µ-opioid receptor [8]. There are numerous contemporary approaches to applying machine learning to chemistry [9–16]. However, the application of language models to this space has led to surprising success in predicting biochemical features such as drug-likeness and protein-ligand interactions [11, 13]. These methods require string representations of molecules, commonly using the Simplified Molecular-Input Line-Entry System (SMILES) [17]. Language models are often trained in an unsupervised manner with the reward function tied to sequence reconstruction, i.e. feeding the model a masked or partial input with the goal of reconstructing the original sequence. It was recently demonstrated that a chemical language model, though trained only on SMILES strings, correctly predicted complex biophysical and quantum-chemical properties [18]. This points to the possibility that these models develop a chemical latent space that allows for the emergence of higher-order biochemical comprehension. Recently, computationally generated chemical libraries have grown to surpass 37 billion commercially available compounds [19]. This marked growth has generated a new field of computational pre-screening of chemicals in order to support resource efficient discovery in the laboratory [20]. One primary class of computational pre-screening methods are chemical similarity searches. These methods have historically used structure-based comparisons, a notable example being the fingerprint Tanimoto search which computes a hierarchical list of molecules ranked by molecular substructure similarity to a given query [21, 22]. Chemical Language Models (CLM) have been applied to drug discovery, in particular de novo molecule generation and chemical similarity searches. De novo methods generate novel molecules with the decoder portion of a language model after fine-tuning toward a specific molecule or target [14–16, 18]. Building off the recent success of generative models such as GPT, de novo molecule generation has shown promise but is limited due to a lack of generalizability and guaranteed synthesizability [23, 24]. In contrast, a chemical similarity search based on a CLM has the advantage of computational speed, generalizability, and high database control to ensure synthesizability. Sellner et al. recently created a novel transformer-based chemical similarity search with an optimized loss function to approximate previous structure-based methods [12]. However, this method tends to identify molecules with high structural similarity to the query molecule, when instead we would often like to find structurally distinct functional analogues. Such a CLM-based search does not currently exist. Here, we describe a chemical similarity search based on a CLM that identifies molecules with similar function to a given query molecule. This method works by calculating the CLM-computed feature vector similarity between a query SMILES string and a chemical database. Keeping the SMILES canonicalization algorithm constant between the query and database resulted in a chemical language search that approximated recent transformer-based chemical search methods. However, we found that when the query SMILES string was canonicalized with a different algorithm than was used for the database, the reliance on structural similarity diminished while functional similarity was retained. This behavior seemed reasonable given the literature describing how models learn to better represent chemical space when SMILES randomization occurs during training and from the reported emergent understanding of underrepresented languages in predominantly English-trained models [25–28]. We utilize alternatively canonicalized queries as a novel prompt engineering strategy to identify structurally distinct functional analogues of small molecules. Our method fundamentally differs from existing literature in that SMILES augmentation was used for the query of a chemical similarity search rather than for model training and fine-tuning to a specific task. We tested our method across three canonicalizations and eight query molecules and found that with increasingly divergent canonicalizations we were able to identify structurally distinct functional analogues. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

Transformer-Based Chemical Search Finds Functional Analogues Beyond Structural Similarity

Authors:
(1) Clayton W. Kosonocky, Department of Molecular Biosciences, The University of Texas at Austin (clayton.kosonocky@utexas.edu);
(2) Aaron L. Feller, Department of Molecular Biosciences, The University of Texas at Austin (aaron.feller@utexas.edu);
(3) Claus O. Wilke, Department of Integrative Biology, The University of Texas at Austin and Corresponding Author (wilke@austin.utexas.edu);
(4) Andrew D. Ellington, Department of Molecular Biosciences, The University of Texas at Austin (ellingtonlab@gmail.com). Table of Links Abstract & Introduction
Methods
Results and Discussion
Determining Whether Canonicalization Impacts Search Behavior
Explanation of Search Behavior & Drawbacks, Future Improvements, and Potential for Misuse
Conclusion, Acknowledgements, Author Contributions, & more.
Supplementary Figures 2. Methods CheSS Overview. The Chemical Semantic Search (CheSS) is a molecular search framework that uses language model-encoded feature vectors to compute similarity scores across molecular space. A database of molecules is encoded as strings using SMILES format [17]. A chemical language model is then used to generate a feature vector for each molecule in the database as well as the query molecule. The cosine similarity between the query vector and each database vector is computed, resulting in a vector of feature cosine similarities. Language Model. ChemBERTa was used as the language model to generate embeddings [29]. This was a Bidirectional Encoder Representations from Transformers (BERT) model with 12 hidden layers of size 768, and was trained on 10M random non-redundant achiral SMILES strings selected from PubChem [30]. ChemBERTa was chosen over newer, higher-parameter BERT models due to the ease of implementation and publicly available dataset. ChemBERTa does not support isomeric SMILES (chirality), and all SMILES were canonicalized before input. Database. The CheSS molecular feature database was built from the ∼10M random achiral molecules used to train ChemBERTa [29]. For each molecule, the SMILES string was canonicalized using RDKit [31]. We reduced this dataset to exclude all SMILES strings that tokenized to more than 512 tokens, the maximum supported by ChemBERTa. This resulted in a database of 9,999,809 molecules. The database SMILES strings were encoded into feature vectors with ChemBERTa. The [CLS] token vector representations of the final layer were chosen to be the feature vectors, as described in the original BERT paper [30]. These feature vectors were then L2 normalized and stored in chunks of 100k SMILES string-feature vector pairs for future cosine similarity calculations. Canonicalization Query Types. ChemBERTa was trained on SMILES strings canonicalized using RDKit [29, 31]. Different canonicalization algorithms result in different, but equally valid, standardized strings representing the same molecule, which we utilize to create three highly different queries for the same molecule. The first query type used RDKit with its default Python implementation settings. This algorithm was used to canonicalize the database & train the model. When converting molecules to SMILES, RDKit allows specification of which atom number to root the SMILES string to. The default is Atom 0, and each atom results in a different representation. The feature cosine similarity was calculated between the default RDKit SMILES and the “Atom n” RDKit SMILES for each atom in the query molecule, as demonstrated in Figure S2. From these, we took the most dissimilar “Atom n” SMILES strings to be the second query type for each molecule. To obtain a third dissimilar SMILES representation, OEChem 2.3.0, a markedly different canonicalization algorithm than RDKit, was used [32]. These SMILES strings were obtained from the PubChem website. Similarity Metrics. Various similarity metrics were used throughout, which include feature cosine similarity, Gestalt pattern matching similarity, fingerprint Tanimoto similarity, token vector length similarity, and token similarity [33, 34]. Feature cosine similarity is a distance metric that calculates the angle between two vectors A and B: A cosine similarity of 1 indicates the normalized vectors are the same, 0 means they are orthogonal to one another, and −1 means they are opposite of one another. Matching characters are identified first from the longest common substring, with recursive counts in non-matching regions on both sides of the substring. The metric ranges from a perfect match of 1 to a completely dissimilar string of 0. We used the difflib Python implementation of the Gestalt pattern matching algorithm to calculate Gestalt similarity. Fingerprint Tanimoto similarity was used to calculate the structural similarity between pairs of molecules. This method encodes substructures into a binary vector, and then calculates the Tanimoto similarity between these encoded vectors. The Tanimoto / Jaccard similarity is the number of shared elements (intersection) between two sets A and B over the total number of unique elements in both sets (union) (Eq. 3): This metric ranges from 1 (all elements shared) to 0 (no elements shared). The RDKit default implementation of fingerprint Tanimoto similarity was used herein. All SMILES were encoded into token vectors before being passed into the model. These tokenized vectors were used for additional comparisons to better understand search behavior. The first metric used from these was the ratio of token lengths between two vectors. The second metric was the token Tanimoto / Jaccard similarity (Eq. 3) between the two molecules’ token vectors, and was used to determine the ratio of shared tokens between the two vectors. This metric ranges from 1 (all tokens shared) to 0 (no tokens shared). Patent & Literature Search. In order to determine known functionality of the molecules examined herein, a patent & literature search was performed. The patents and literature articles for each molecule, if available, was obtained from PubChem [35]. A comprehensive list of all molecules considered herein, & their associated patents, is provided in Table S2. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. Authors: (1) Clayton W. Kosonocky, Department of Molecular Biosciences, The University of Texas at Austin (clayton.kosonocky@utexas.edu); (2) Aaron L. Feller, Department of Molecular Biosciences, The University of Texas at Austin (aaron.feller@utexas.edu); (3) Claus O. Wilke, Department of Integrative Biology, The University of Texas at Austin and Corresponding Author (wilke@austin.utexas.edu); (4) Andrew D. Ellington, Department of Molecular Biosciences, The University of Texas at Austin (ellingtonlab@gmail.com). Authors: Authors: (1) Clayton W. Kosonocky, Department of Molecular Biosciences, The University of Texas at Austin (clayton.kosonocky@utexas.edu); (2) Aaron L. Feller, Department of Molecular Biosciences, The University of Texas at Austin (aaron.feller@utexas.edu); (3) Claus O. Wilke, Department of Integrative Biology, The University of Texas at Austin and Corresponding Author (wilke@austin.utexas.edu); (4) Andrew D. Ellington, Department of Molecular Biosciences, The University of Texas at Austin (ellingtonlab@gmail.com). Table of Links Abstract & Introduction Methods Results and Discussion Determining Whether Canonicalization Impacts Search Behavior Explanation of Search Behavior & Drawbacks, Future Improvements, and Potential for Misuse Conclusion, Acknowledgements, Author Contributions, & more. Supplementary Figures Abstract & Introduction Abstract & Introduction Methods Methods Results and Discussion Results and Discussion Determining Whether Canonicalization Impacts Search Behavior Determining Whether Canonicalization Impacts Search Behavior Explanation of Search Behavior & Drawbacks, Future Improvements, and Potential for Misuse Explanation of Search Behavior & Drawbacks, Future Improvements, and Potential for Misuse Conclusion, Acknowledgements, Author Contributions, & more. Conclusion, Acknowledgements, Author Contributions, & more. Supplementary Figures Supplementary Figures 2. Methods CheSS Overview. The Chemical Semantic Search (CheSS) is a molecular search framework that uses language model-encoded feature vectors to compute similarity scores across molecular space. A database of molecules is encoded as strings using SMILES format [17]. A chemical language model is then used to generate a feature vector for each molecule in the database as well as the query molecule. The cosine similarity between the query vector and each database vector is computed, resulting in a vector of feature cosine similarities. CheSS Overview. Language Model. ChemBERTa was used as the language model to generate embeddings [29]. This was a Bidirectional Encoder Representations from Transformers (BERT) model with 12 hidden layers of size 768, and was trained on 10M random non-redundant achiral SMILES strings selected from PubChem [30]. ChemBERTa was chosen over newer, higher-parameter BERT models due to the ease of implementation and publicly available dataset. ChemBERTa does not support isomeric SMILES (chirality), and all SMILES were canonicalized before input. Language Model. Database . The CheSS molecular feature database was built from the ∼10M random achiral molecules used to train ChemBERTa [29]. For each molecule, the SMILES string was canonicalized using RDKit [31]. We reduced this dataset to exclude all SMILES strings that tokenized to more than 512 tokens, the maximum supported by ChemBERTa. This resulted in a database of 9,999,809 molecules. The database SMILES strings were encoded into feature vectors with ChemBERTa. The [CLS] token vector representations of the final layer were chosen to be the feature vectors, as described in the original BERT paper [30]. These feature vectors were then L2 normalized and stored in chunks of 100k SMILES string-feature vector pairs for future cosine similarity calculations. Database Canonicalization Query Types. ChemBERTa was trained on SMILES strings canonicalized using RDKit [29, 31]. Different canonicalization algorithms result in different, but equally valid, standardized strings representing the same molecule, which we utilize to create three highly different queries for the same molecule. The first query type used RDKit with its default Python implementation settings. This algorithm was used to canonicalize the database & train the model. When converting molecules to SMILES, RDKit allows specification of which atom number to root the SMILES string to. The default is Atom 0, and each atom results in a different representation. The feature cosine similarity was calculated between the default RDKit SMILES and the “Atom n” RDKit SMILES for each atom in the query molecule, as demonstrated in Figure S2. From these, we took the most dissimilar “Atom n” SMILES strings to be the second query type for each molecule. To obtain a third dissimilar SMILES representation, OEChem 2.3.0, a markedly different canonicalization algorithm than RDKit, was used [32]. These SMILES strings were obtained from the PubChem website. Canonicalization Query Types. Similarity Metrics. Various similarity metrics were used throughout, which include feature cosine similarity, Gestalt pattern matching similarity, fingerprint Tanimoto similarity, token vector length similarity, and token similarity [33, 34]. Feature cosine similarity is a distance metric that calculates the angle between two vectors A and B: Similarity Metrics. A cosine similarity of 1 indicates the normalized vectors are the same, 0 means they are orthogonal to one another, and −1 means they are opposite of one another. Matching characters are identified first from the longest common substring, with recursive counts in non-matching regions on both sides of the substring. The metric ranges from a perfect match of 1 to a completely dissimilar string of 0. We used the difflib Python implementation of the Gestalt pattern matching algorithm to calculate Gestalt similarity. Fingerprint Tanimoto similarity was used to calculate the structural similarity between pairs of molecules. This method encodes substructures into a binary vector, and then calculates the Tanimoto similarity between these encoded vectors. The Tanimoto / Jaccard similarity is the number of shared elements (intersection) between two sets A and B over the total number of unique elements in both sets (union) (Eq. 3): This metric ranges from 1 (all elements shared) to 0 (no elements shared). The RDKit default implementation of fingerprint Tanimoto similarity was used herein. All SMILES were encoded into token vectors before being passed into the model. These tokenized vectors were used for additional comparisons to better understand search behavior. The first metric used from these was the ratio of token lengths between two vectors. The second metric was the token Tanimoto / Jaccard similarity (Eq. 3) between the two molecules’ token vectors, and was used to determine the ratio of shared tokens between the two vectors. This metric ranges from 1 (all tokens shared) to 0 (no tokens shared). Patent & Literature Search. In order to determine known functionality of the molecules examined herein, a patent & literature search was performed. The patents and literature articles for each molecule, if available, was obtained from PubChem [35]. A comprehensive list of all molecules considered herein, & their associated patents, is provided in Table S2. Patent & Literature Search. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. This paper is available on arxiv under CC BY-NC-SA 4.0 DEED license. available on arxiv

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