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<p>Navigating the vast chemical space to identify potent therapeutic agents with optimal pharmacokinetic properties remains a formidable bottleneck in pharmaceutical development. While virtual screening is limited to the exploration of existing chemical libraries, de novo design offers a complementary approach by constructing novel chemical entities “from scratch,” potentially accessing unexplored regions of the chemical universe. De novo design may lead to novel scaffolds or compounds when compared to searching the compounds from chemical libraries. This study introduces LigGen, a novel tool for de novo drug design&#xa0;(DNDD) that combines fragment-based drug design (FBDD) with recurrent neural networks (RNNs) and Monte Carlo Simulated Annealing (MCSA) to generate ligands for target proteins. LigGen is effective in generating novel and synthesizeable high-affinity ligands for various biological drug-targets.&#xa0; The efficacy of the tool is demonstrated by testing it on CrossDocked dataset, as well as by generating ligands for proteins Beta-secretase 1 (BACE1), Monoamine Oxidase B (MAO-B), and Acetylcholinesterase (AChE). LigGen presents a promising approach for advancing drug discovery, offering a robust platform for novel drug design.</p>

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LigGen—a GEN-AI based ligand generation approach for de-novo drug design

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月2日

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  • Anshul Yadav,
  • Natarajan Arul Murugan

摘要

Navigating the vast chemical space to identify potent therapeutic agents with optimal pharmacokinetic properties remains a formidable bottleneck in pharmaceutical development. While virtual screening is limited to the exploration of existing chemical libraries, de novo design offers a complementary approach by constructing novel chemical entities “from scratch,” potentially accessing unexplored regions of the chemical universe. De novo design may lead to novel scaffolds or compounds when compared to searching the compounds from chemical libraries. This study introduces LigGen, a novel tool for de novo drug design (DNDD) that combines fragment-based drug design (FBDD) with recurrent neural networks (RNNs) and Monte Carlo Simulated Annealing (MCSA) to generate ligands for target proteins. LigGen is effective in generating novel and synthesizeable high-affinity ligands for various biological drug-targets.  The efficacy of the tool is demonstrated by testing it on CrossDocked dataset, as well as by generating ligands for proteins Beta-secretase 1 (BACE1), Monoamine Oxidase B (MAO-B), and Acetylcholinesterase (AChE). LigGen presents a promising approach for advancing drug discovery, offering a robust platform for novel drug design.