DrugPose: benchmarking 3D generative methods for early stage drug discovery

Abstract

Molecule generation in 3D space has gained attention in the past few years. These models typically have a hypothesis that they need to satisfy (i.e. shape) or they are designed to fit into a protein pocket. However, there's been limited evaluation of the 3D poses they produce. In the previous work, the generated molecules are redocked and the generated poses are disregarded. Moreover, many of the generated molecules are not synthesisable and druglike. To tackle these challenges we propose DrugPose, a novel benchmark framework, that utilises Simbind to evaluate the generated molecules based on their coherence with the initial hypothesis formed from available data (e.g., active compounds and protein structures) and their adherence to the laws of physics. Moreover, it offers enhanced insights into synthesizability by directly cross-referencing with a commercial database and utilising the Ghose filter for assessing drug-likeness. Considering current generative methods, the percentage of generated molecules with the intended binding mode ranges from 4.7% to 15.9%, with commercial accessibility spanning 23.6% to 38.8% and fully satisfying the Ghose filter between 10% and 40%. These results highlight the need for further research to develop more reliable and transparent methodologies for 3D molecule generation.