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MolCRAFT: Structure-Based Drug Design in Continuous Parameter Space
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Generative models for structure-based drug design (SBDD) have shown promising results in recent years. Existing works mainly focus on how to generate molecules with higher binding affinity, ignoring the feasibility prerequisites for generated 3D poses and resulting in false positives. We conduct thorough studies on key factors of ill-conformational problems when applying autoregressive methods and diffusion to SBDD, including mode collapse and hybrid continuous-discrete space. In this paper, we introduce MolCRAFT, the first SBDD model that operates in the continuous parameter space, together with a novel noise reduced sampling strategy. Empirical results show that our model consistently achieves superior performance in binding affinity with more stable 3D structure, demonstrating our ability to accurately model interatomic interactions. To our best knowledge, MolCRAFT is the first to achieve reference-level Vina Scores (-6.59 kcal/mol) with comparable molecular size, outperforming other strong baselines by a wide margin (-0.84 kcal/mol). Code is available at https://github.com/AlgoMole/MolCRAFT.
Forward citations
Cited by 6 Pith papers
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Routing by Reaching: Composition of Pre-trained GFlowNets for Multi-Objective Generation
A reaching-probability-weighted mixture of pre-trained GFlowNet policies exactly samples weighted-sum reward combinations at inference time and approximates logical operators such as conjunction and contrast.
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FLOWR.root: A flow matching based foundation model for joint multi-purpose structure-aware 3D ligand generation and affinity prediction
A flow-matching model jointly generates pocket-aware 3D ligands and predicts their binding affinities, reporting state-of-the-art generation and competitive affinity accuracy with a speed advantage.
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IBEX: Information-Bottleneck-EXplored Coarse-to-Fine Molecular Generation under Limited Data
IBEX trains a 3D diffusion model on scaffold-hopping tasks and refines generated poses with a six-degree-of-freedom physics optimization, raising zero-shot docking success from 53% to 64% on CBGBench.
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MolFORM: Multi-modal Flow Matching for Structure-Based Drug Design
A flow-matching model with direct preference optimization fine-tuning generates protein-binding molecules faster than diffusion baselines, with improved docking scores on the CrossDocked2020 benchmark.
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Reimagining Target-Aware Molecular Generation through Retrieval-Enhanced Aligned Diffusion
READ couples contrastively aligned latent diffusion with pocket-similarity retrieval to generate 3D ligands, reporting Rank 1 on CBGBench and lower Vina energies than native ligands.
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MolPIF: A Parameter Interpolation Flow Model for Molecule Generation
MolPIF generates 3D ligands by interpolating the parameters of Gaussian coordinate and Dirichlet atom-type distributions, reporting stronger docking scores and geometric fidelity than prior flow and diffusion models o...
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