A universal model for drug-receptor interactions

The modern AI models promise decoding of the genomic landscape that holds, in principle, the information required for rational therapeutic design. Genes encode proteins whose functions are mediated by their three-dimensional structures via bonded and non-bonded interactions. Since the late 1970s, the advent of macromolecular crystallography inspired the notion that structural knowledge alone could enable a “lock-and-key” approach to drug design. However, this framework has failed to catalyze a step-change in generating new drugs. Drug discovery continues to depend on costly, resource-intensive, and largely serendipitous screening campaigns that probe only an infinitesimal fraction of the drug-like chemical space. Despite some success cases, our understanding of, and reasoning from, non-bonded interaction chemistry is still too limited for generalized applicability. Furthermore, though structural databases contain hundreds of thousands of entries, a strong historical bias pervades protein-drug structures, hindering reliable advances through data science. Here, we show how a simple machine learning model successfully infers the principles of non-bonded chemical interactions in the drug-receptor space. A reductionist approach to the training data led to a model generalizing drug-target interactions, minimizing memorization frequently seen in large, structural models. We show how the model can infer complex interactions incomprehensible for classical physics-based force field models and approach a quantum level of understanding. The model was validated by retrospective and prospective, real-life problems in drug optimization. when targeting a challenging protein-protein interface. Our approach offers a simple, interpretable and explainable way to steer drug optimization and condition complex generative models to greatly accelerate, diversify and enhance drug discovery.