The AVOID-OME: Speeding drug discovery by unlocking machine learning on anti-targets

Small molecule therapies represent a significant portion of FDA-approved drugs, yet their development is often hindered by the challenge of balancing on-target activity with desirable pharmacokinetic (PK) properties. Pharmacokinetic properties, which encompass absorption, distribution, metabolism, and excretion (ADME), determine a drug's fate within the body and are crucial for its efficacy and safety. Currently, the optimization of these properties is often addressed late in the pre-clinical drug discovery process, leading to costly failures. We are working to overcome this limitation by proactively characterizing the chemical space accessible to ADMET-associated proteins ("anti-targets"). By applying recent advances in experimental and computational structural biology, a comprehensive open library of experimental and structural datasets are being generated. This precompetitive resource will provide valuable insights into the binding properties of "anti-targets" and empower researchers to develop predictive AI models for pharmacokinetic optimization. This shift towards a more proactive and data-driven approach could streamline lead optimization, mitigate late-stage attrition, and ultimately accelerate the delivery of new therapies to patients.