Structure-Based Drug Design with a Deep Hierarchical Generative Model

Recently, the remarkable growth of available crystal
structure
data and libraries of commercially available or readily synthesizable
molecules have unlocked previously inaccessible regions of chemical
space for drug development. Paired with improvements in virtual ligand
screening methods, these expanded libraries are having a notable impact
on early drug design efforts. Yet screening-based methods still face
scalability limits, due to computational constraints and the sheer
scale of drug-like space. Machine learning approaches are overcoming
these limitations by learning the fundamental intra- and intermolecular
relationships in drug-target systems from existing data. Here, we
introduce DrugHIVE, a deep hierarchical variational autoencoder that
outperforms state-of-the-art autoregressive and diffusion-based methods
in both speed and performance on common generative benchmarks. DrugHIVE’s
hierarchical design enables improved control over molecular generation.
Its capabilities include dramatically increasing virtual screening
efficiency and accelerating a wide range of common drug design tasks,
including de novo generation, molecular optimization, scaffold hopping,
linker design, and high-throughput pattern replacement. Our highly
scalable method can even be applied to receptors with high-confidence
AlphaFold-predicted structures, extending the ability to generate
high-quality drug-like molecules to a majority of the unsolved human
proteome.