AI in Bio
Artificial intelligence (AI) in drug discovery and biotech research has evolved beyond the proof-of-concept stage. What we now witness is the assembly of a layered, technical foundation: model architectures, data pipelines, experimental validation, clinical trial design, and regulatory alignment.
The definition of AI drug discovery has evolved, too. No longer confined to QSAR models or target prediction tools, modern platforms leverage various types of predictive and generative models, including so-called foundation models, to map biological processes at a holistic level.
Some of the emerging end-to-end systems are now capable of connecting various -omics data types as well as clinical data and literature data, enabling novel ways of hypothesis generation, target discovery, and biomarker discovery. Numerous AI systems include chemical design modules, deeply integrated with biology modules, enabling molecular design and lead optimization across various therapeutic modalities: small molecules, macrocycles, proximity-inducing compounds (e.g., PROTACs, molecular glues), antibodies, RNA and DNA-based therapies, vaccines, and more.
Despite long-standing optimism and even hype about how AI can improve drug discovery, the reality of AI-inspired clinical pipelines is relatively modest at present. One of BiopharmaTrend’s reports, “It’s Been a Decade of AI in the Drug Discovery Race. What’s Next?”, explored how a decade of progress in the AI drug discovery space led to a series of disappointments with AI-inspired clinical stage drug candidates. The gap between modeling output and therapeutic proof remains one of the field’s central challenges.
Beyond preclinical research, there are also AI-based systems built to help improve the design and operations of clinical trials, as well as help predict the probabilities of their success. Assessment of the impact of AI on clinical trials is a work in progress, as there are many contradictory facts and developments.
Still, the momentum is real, and to understand the industry dynamics, here we publish news, developments, commentaries, breakthroughs, and company analytics about everything that relates to the advent of artificial intelligence and data-first strategies in the life sciences.
However, refer to Tech Giants for everything that involves activities of the largest tech corporations (think Google, NVIDIA, Microsoft, Tencent, Intel, Samsung, etc) in the biomedical and healthcare space.
For insights related to the strategic moves by the largest pharma, biotech, and contract research corporations (think Eli Lilly, GSK, Biogen, and Charles River Laboratories, refer to the Industry Movers folder.
