TAGMol: Target-Aware Gradient-guided Molecule Generation Article Swipe

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Vineeth Dorna , D. Subhalingam , Keshav Kolluru , Shreshth Tuli , Mrityunjay Singh , Saurabh Singal , N. M. Anoop Krishnan , Sayan Ranu ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2406.01650

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.

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Molecule Chemistry Computer science Organic chemistry

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2406.01650
PDF: https://arxiv.org/pdf/2406.01650
OA Status: green
Cited By: 2
Related Works: 10
OpenAlex ID: https://openalex.org/W4401102983

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OpenAlex ID: https://openalex.org/W4401102983

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DOI: https://doi.org/10.48550/arxiv.2406.01650

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Title: TAGMol: Target-Aware Gradient-guided Molecule Generation

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-06-03

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Authors: Vineeth Dorna, D. Subhalingam, Keshav Kolluru, Shreshth Tuli, Mrityunjay Singh, Saurabh Singal, N. M. Anoop Krishnan, Sayan Ranu

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Landing page: https://arxiv.org/abs/2406.01650

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PDF URL: https://arxiv.org/pdf/2406.01650

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Concepts: Molecule, Chemistry, Computer science, Organic chemistry

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Cited by: 2

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Citations by year (recent): 2025: 2

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Related works (count): 10

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