VALID-Mol: a Systematic Framework for Validated LLM-Assisted Molecular Design
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866911457629700096 |
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| author | Malikussaid Nuha, Hilal Hudan Kurniawan, Isman |
| author_facet | Malikussaid Nuha, Hilal Hudan Kurniawan, Isman |
| contents | Large Language Models demonstrate substantial promise for advancing scientific discovery, yet their deployment in disciplines demanding factual precision and specialized domain constraints presents significant challenges. Within molecular design for pharmaceutical development, these models can propose innovative molecular modifications but frequently generate chemically infeasible structures. We introduce VALID-Mol, a comprehensive framework that integrates chemical validation with LLM-driven molecular design, achieving an improvement in valid chemical structure generation from 3% to 83%. Our methodology synthesizes systematic prompt optimization, automated chemical verification, and domain-adapted fine-tuning to ensure dependable generation of synthesizable molecules with enhanced properties. Our contribution extends beyond implementation details to provide a transferable methodology for scientifically-constrained LLM applications with measurable reliability enhancements. Computational analyses indicate our framework generates promising synthesis candidates with up to 17-fold predicted improvements in target binding affinity while preserving synthetic feasibility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_23339 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VALID-Mol: a Systematic Framework for Validated LLM-Assisted Molecular Design Malikussaid Nuha, Hilal Hudan Kurniawan, Isman Machine Learning Artificial Intelligence Chemical Physics Quantitative Methods 68T50 (Primary) 92E10, 68T07 (Secondary) I.2.7; J.3; I.2.1; I.2.6 Large Language Models demonstrate substantial promise for advancing scientific discovery, yet their deployment in disciplines demanding factual precision and specialized domain constraints presents significant challenges. Within molecular design for pharmaceutical development, these models can propose innovative molecular modifications but frequently generate chemically infeasible structures. We introduce VALID-Mol, a comprehensive framework that integrates chemical validation with LLM-driven molecular design, achieving an improvement in valid chemical structure generation from 3% to 83%. Our methodology synthesizes systematic prompt optimization, automated chemical verification, and domain-adapted fine-tuning to ensure dependable generation of synthesizable molecules with enhanced properties. Our contribution extends beyond implementation details to provide a transferable methodology for scientifically-constrained LLM applications with measurable reliability enhancements. Computational analyses indicate our framework generates promising synthesis candidates with up to 17-fold predicted improvements in target binding affinity while preserving synthetic feasibility. |
| title | VALID-Mol: a Systematic Framework for Validated LLM-Assisted Molecular Design |
| topic | Machine Learning Artificial Intelligence Chemical Physics Quantitative Methods 68T50 (Primary) 92E10, 68T07 (Secondary) I.2.7; J.3; I.2.1; I.2.6 |
| url | https://arxiv.org/abs/2506.23339 |