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

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

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