ShEPhERD: Diffusing shape, electrostatics, and pharmacophores for bioisosteric drug design
This work addresses the challenge of 3D interaction-aware chemical design for drug discovery, presenting a novel generative approach that could impact ligand-based drug design, though it appears incremental as it builds on existing diffusion models and similarity scoring functions.
The authors tackled the problem of generating molecules with specific 3D interaction profiles for drug design by developing ShEPhERD, an SE(3)-equivariant diffusion model that jointly diffuses molecular structures and interaction features, demonstrating its potential through tasks like natural product ligand hopping and bioisosteric fragment merging.
Engineering molecules to exhibit precise 3D intermolecular interactions with their environment forms the basis of chemical design. In ligand-based drug design, bioisosteric analogues of known bioactive hits are often identified by virtually screening chemical libraries with shape, electrostatic, and pharmacophore similarity scoring functions. We instead hypothesize that a generative model which learns the joint distribution over 3D molecular structures and their interaction profiles may facilitate 3D interaction-aware chemical design. We specifically design ShEPhERD, an SE(3)-equivariant diffusion model which jointly diffuses/denoises 3D molecular graphs and representations of their shapes, electrostatic potential surfaces, and (directional) pharmacophores to/from Gaussian noise. Inspired by traditional ligand discovery, we compose 3D similarity scoring functions to assess ShEPhERD's ability to conditionally generate novel molecules with desired interaction profiles. We demonstrate ShEPhERD's potential for impact via exemplary drug design tasks including natural product ligand hopping, protein-blind bioactive hit diversification, and bioisosteric fragment merging.