Evaluating Molecular Similarity Measures: Do Similarity Measures Reflect Electronic Structure Properties? DOI
Rebekah Duke, Chih-Hsuan Yang, Baskar Ganapathysubramanian

et al.

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: April 29, 2025

The rapid adoption of big data, machine learning (ML), and generative artificial intelligence (AI) in chemical discovery has heightened the importance quantifying molecular similarity. Molecular similarity, commonly assessed as distance between fingerprints, is integral to applications such database curation, diversity analysis, property prediction. AI tools frequently rely on these similarity measures cluster molecules under assumption that structurally similar exhibit properties. However, this not universally valid, particularly for continuous properties like electronic structure Despite prevalence fingerprint-based measures, their evaluation largely depended biological activity data sets qualitative metrics, limiting relevance nonbiological domains. To address gap, we propose a framework evaluate correlation Our approach builds concept neighborhood behavior incorporates kernel density estimation (KDE) analysis quantify how well capture relationships. Using set over 350 million molecule pairs with structure, redox, optical properties, systematically several fingerprint generators, functions, Both curated are publicly available.

Language: Английский

AI/ML methodologies and the future-will they be successful in designing the next generation of new chemical entities? DOI Creative Commons
Rachelle J. Bienstock

Journal of Cheminformatics, Journal Year: 2025, Volume and Issue: 17(1)

Published: April 6, 2025

Cheminformatics and chemical databases are essential to drug discovery. However, machine learning (ML) artificial intelligence (AI) methodologies changing the way in which data is used. How will use of change discovery moving forward? do new ML methods molecular property prediction, hit lead target identification structure prediction differ compare with previous computational methods? Will improve diversity ligand design, offer enhancements. There still many advantages physics based they something lacking ML/ AI methods. Additionally, training often give best results when experimental assay measurements fed back into model. Often modeling not diametrically opposed but greatest advantage used complementary.

Language: Английский

Citations

0
Evaluating Molecular Similarity Measures: Do Similarity Measures Reflect Electronic Structure Properties? DOI
Rebekah Duke, Chih-Hsuan Yang, Baskar Ganapathysubramanian

et al.

Journal of Chemical Information and Modeling, Journal Year: 2025, Volume and Issue: unknown

Published: April 29, 2025

The rapid adoption of big data, machine learning (ML), and generative artificial intelligence (AI) in chemical discovery has heightened the importance quantifying molecular similarity. Molecular similarity, commonly assessed as distance between fingerprints, is integral to applications such database curation, diversity analysis, property prediction. AI tools frequently rely on these similarity measures cluster molecules under assumption that structurally similar exhibit properties. However, this not universally valid, particularly for continuous properties like electronic structure Despite prevalence fingerprint-based measures, their evaluation largely depended biological activity data sets qualitative metrics, limiting relevance nonbiological domains. To address gap, we propose a framework evaluate correlation Our approach builds concept neighborhood behavior incorporates kernel density estimation (KDE) analysis quantify how well capture relationships. Using set over 350 million molecule pairs with structure, redox, optical properties, systematically several fingerprint generators, functions, Both curated are publicly available.

Language: Английский

Citations

0