In this post I tried to summarize pretty much all small-scale companies (mostly startups), who offer artificial intelligence (AI)-driven computational tools and services for the key stages of a drug discovery pipeline.
Excitement about artificial intelligence has been growing in the pharmaceutical industry over the last decade or so -- thanks to rapid progress in the following areas: computer hardware (better and cheaper processors); cloud infrastructures and distributed computing; natural language processing (NLP), and most vividly -- deep learning (DL) algorithms and neural net architectures. This allowed AI to emerge from mostly theoretical discipline of the last century to workable and widely commercialized toolset, nowadays.
Needless to say progress in AI goes shoulder to shoulder with advancements in automation -- novel robotized mechanisms, sensor systems, and new creative ways to connect processes, and integrate different data types (e.g. “omics” with EMRs/EHRs).
Altogether, the above advances in AI and automation represent a new world of opportunities for the pharmaceutical and biotech companies, who express a growing interest to adopt AI solutions for boosting their R&D, and operational functions.
Therefore, this post is meant to be a quick “go-to” reference for choosing the right AI-vendor for a wide range of pharmaceutical research tasks. Note the following before you proceed:
the companies are grouped according to a typical use-case, and each company profile features condensed information about the organization and what it does (click on it to view).
any one company may be featured in multiple chapters, since many AI-vendors are building integrated solutions for overlapping use-cases.
If you want your company’s logo to be featured in this list, please, send us a request with copyright/trademark permission to do so.
In case you need access to an interactive market intelligence tool, with automatically updated reports about AI-vendors and more than 10.000 other companies in the biopharma industry -- subscribe to become a beta-user of BPT Analytics platform (Start of private beta testing period: February 2020).
Getting research materials, consumables, and R&D services
Data mining / Ontology building
Biology research (Target identification/validation)
Virtual Lab Assistants
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You may want to read “How Big Pharma Adopts AI To Boost Drug Discovery” to get an extra insight into application use cases and examples of R&D partnerships in this space.
Sources of information: BPT Analytics; Crunchbase; company websites; PR wire services; Google News;