Despite significant investment over the past decade, only a handful of companies are now bringing clinical candidates discovered with the assistance of AI/ML forward. Though there’s a looseness to what should count as an “AI-discovered drug,” here we highlight internally-generated drug programs from companies focused on leveraging AI/ML in drug discovery, who have explicitly stated that AI/ML methods were involved in the discovery process. These include but are not limited to:
- A SHP2 inhibitor discovered using molecular dynamics simulations of proteins
- A CNS-penetrant HDAC inhibitor and allosteric MEK inhibitor in nontraditional indications
- A pan-Trk inhibitor discovered using literature data-mining to support target ID
For a review of the history of AI/ML in drug discovery and more resources on the topic, check out part I, and for preclinical small molecule AI/ML companies, check out part III of this series.
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