Back to Articles
Why Binding Affinity?
Why Binding Affinity? Disrupting the physics-based Pareto front through AI and data The challenges of AI-driven Structure-Based-Drug-Design What Nesso-1 unlocks, the remaining problems and the road ahead We recently released Nesso-1, a model designed to predict binding affinity more efficiently than any existing cofolding-based open source model. Why focus on binding affinity? It’s a key problem in small molecule drug design. Specifically, the challenge is determining if a compound will bind to a specific target (and/or off-target), how strongly it will bind, and the concentration required to elicit a biological response. Once a disease target has been identified, the goal is often to inhibit or activate it, requiring the lowest possible dose to be efficacious whilst minimizing unwanted side-effects. While drug-discovery is a complex multi-optimization problem—for example, a compound binding to a target may lack key pharmacological properties or be too synthetically complex—many challenges become more relevant after strong binders have been found. Moreover, other risks affecting a drug candidate, such as off-target toxicity—stemming from a compound binding to other targets, with undesirable biological effects—could be significantly mitigated by being able to predict affinity at scale and accurately across the targetome.
