TAGMol: Target-Aware Gradient-guided Molecule Generation
Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:3OBF7ZDUrecord.jsonopen to challenge →
read the original abstract
3D generative models have shown significant promise in structure-based drug design (SBDD), particularly in discovering ligands tailored to specific target binding sites. Existing algorithms often focus primarily on ligand-target binding, characterized by binding affinity. Moreover, models trained solely on target-ligand distribution may fall short in addressing the broader objectives of drug discovery, such as the development of novel ligands with desired properties like drug-likeness, and synthesizability, underscoring the multifaceted nature of the drug design process. To overcome these challenges, we decouple the problem into molecular generation and property prediction. The latter synergistically guides the diffusion sampling process, facilitating guided diffusion and resulting in the creation of meaningful molecules with the desired properties. We call this guided molecular generation process as TAGMol. Through experiments on benchmark datasets, TAGMol demonstrates superior performance compared to state-of-the-art baselines, achieving a 22% improvement in average Vina Score and yielding favorable outcomes in essential auxiliary properties. This establishes TAGMol as a comprehensive framework for drug generation.
This paper has not been read by Pith yet.
Forward citations
Cited by 5 Pith papers
-
Demystifying Multimodal Biomolecular Co-design With Intrinsic Geodesic Coupling
GeoCoupling optimizes temporal couplings between modalities in biomolecular generative models and outperforms synchronous baselines on drug design and protein design tasks.
-
Fine-tuning Pocket-Aware Diffusion Models via Denoising Policy Optimization
DEPPA reformulates the denoising process of pocket-aware diffusion models as a multi-step MDP and applies RL fine-tuning with a coarse scheduler to optimize ligands for binding affinity, drug-likeness, synthesizabilit...
-
ToolMol: Evolutionary Agentic Framework for Multi-objective Drug Discovery
ToolMol integrates evolutionary algorithms with agentic LLMs and precise RDKit tools to optimize multi-objective drug properties, yielding ligands with over 10% better predicted binding affinity and 35% gains in absol...
-
ToolMol: Evolutionary Agentic Framework for Multi-objective Drug Discovery
ToolMol is an evolutionary agentic framework that pairs multi-objective genetic algorithms with LLM tool-calling to generate drug-like ligands with over 10% better predicted binding affinity and 35% better ABFE scores...
-
Fine-Tuning Diffusion Models for Molecular Generation via Reinforcement Learning and Fast Sampling
FTDiff applies GRPO-style RL fine-tuning and fast sampling to a time-free pretrained diffusion model to generate valid diverse high-quality molecules balancing multiple drug design objectives in SBDD.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.