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Integrating computational lead optimization diagnostics with analog design and candidate selection

AIM: Combining computational lead optimization diagnostics with analog design and computational approaches for assessing optimization efforts are discussed and the compound optimization monitor is introduced. METHODS: Approaches for compound potency prediction are described and a new analog design a...

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Autores principales: Yonchev, Dimitar, Bajorath, Jürgen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Future Science Ltd 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7050602/
https://www.ncbi.nlm.nih.gov/pubmed/32140250
http://dx.doi.org/10.2144/fsoa-2019-0131
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author Yonchev, Dimitar
Bajorath, Jürgen
author_facet Yonchev, Dimitar
Bajorath, Jürgen
author_sort Yonchev, Dimitar
collection PubMed
description AIM: Combining computational lead optimization diagnostics with analog design and computational approaches for assessing optimization efforts are discussed and the compound optimization monitor is introduced. METHODS: Approaches for compound potency prediction are described and a new analog design algorithm is introduced. Calculation protocols are detailed. RESULTS & DISCUSSION: The study rationale is explained. Compound optimization monitor diagnostics are combined with a thoroughly evaluated approach for compound design and candidate prioritization. The diagnostic scoring scheme is further extended. FUTURE PERSPECTIVE: Opportunities for practical applications of the integrated computational methodology are described and further development perspectives are discussed.
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spelling pubmed-70506022020-03-05 Integrating computational lead optimization diagnostics with analog design and candidate selection Yonchev, Dimitar Bajorath, Jürgen Future Sci OA Methodology AIM: Combining computational lead optimization diagnostics with analog design and computational approaches for assessing optimization efforts are discussed and the compound optimization monitor is introduced. METHODS: Approaches for compound potency prediction are described and a new analog design algorithm is introduced. Calculation protocols are detailed. RESULTS & DISCUSSION: The study rationale is explained. Compound optimization monitor diagnostics are combined with a thoroughly evaluated approach for compound design and candidate prioritization. The diagnostic scoring scheme is further extended. FUTURE PERSPECTIVE: Opportunities for practical applications of the integrated computational methodology are described and further development perspectives are discussed. Future Science Ltd 2020-01-24 /pmc/articles/PMC7050602/ /pubmed/32140250 http://dx.doi.org/10.2144/fsoa-2019-0131 Text en © 2020 Jürgen Bajorath This work is licensed under the Creative Commons Attribution 4.0 License (http://creativecommons.org/licenses/by/4.0/)
spellingShingle Methodology
Yonchev, Dimitar
Bajorath, Jürgen
Integrating computational lead optimization diagnostics with analog design and candidate selection
title Integrating computational lead optimization diagnostics with analog design and candidate selection
title_full Integrating computational lead optimization diagnostics with analog design and candidate selection
title_fullStr Integrating computational lead optimization diagnostics with analog design and candidate selection
title_full_unstemmed Integrating computational lead optimization diagnostics with analog design and candidate selection
title_short Integrating computational lead optimization diagnostics with analog design and candidate selection
title_sort integrating computational lead optimization diagnostics with analog design and candidate selection
topic Methodology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7050602/
https://www.ncbi.nlm.nih.gov/pubmed/32140250
http://dx.doi.org/10.2144/fsoa-2019-0131
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