PILOT: Equivariant diffusion for pocket conditioned de novo ligand generation with multi-objective guidance via importance sampling

The generation of ligands that both are tailored to a given protein pocket and exhibit a range of desired chemical properties is a major challenge in structure-based drug design. Here, we propose an in-silico approach for the $\textit{de novo}$ generation of 3D ligand structures using the equivariant diffusion model PILOT, combining pocket conditioning with a large-scale pre-training and property guidance. Its multi-objective trajectory-based importance sampling strategy is designed to direct the model towards molecules that not only exhibit desired characteristics such as increased binding affinity for a given protein pocket but also maintains high synthetic accessibility. This ensures the practicality of sampled molecules, thus maximizing their potential for the drug discovery pipeline. PILOT significantly outperforms existing methods across various metrics on the common benchmark dataset CrossDocked2020. Moreover, we employ PILOT to generate novel ligands for unseen protein pockets from the Kinodata-3D dataset, which encompasses a substantial portion of the human kinome. The generated structures exhibit predicted $IC_{50}$ values indicative of potent biological activity, which highlights the potential of PILOT as a powerful tool for structure-based drug design.

Navigating the Design Space of Equivariant Diffusion-Based Generative Models for De Novo 3D Molecule Generation

Deep generative diffusion models are a promising avenue for de novo 3D molecular design in material science and drug discovery. However, their utility is still constrained by suboptimal performance with large molecular structures and limited training data. Addressing this gap, we explore the design space of E(3) equivariant diffusion models, focusing on previously blank spots. Our extensive comparative analysis evaluates the interplay between continuous and discrete state spaces. Out of this investigation, we introduce the EQGAT-diff model, which consistently surpasses the performance of established models on the QM9 and GEOM-Drugs datasets by a large margin. Distinctively, EQGAT-diff takes continuous atomic positions while chemical elements and bond types are categorical and employ a time-dependent loss weighting that significantly increases training convergence and the quality of generated samples. To further strengthen the applicability of diffusion models to limited training data, we examine the transferability of EQGAT-diff trained on the large PubChem3D dataset with implicit hydrogens to target distributions with explicit hydrogens. Fine-tuning EQGAT-diff for a couple of iterations further pushes state-of-the-art performance across datasets. We envision that our findings will find applications in structure-based drug design, where the accuracy of generative models for small datasets of complex molecules is critical.