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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Future Science Ltd
2020
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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. |
format | Online Article Text |
id | pubmed-7050602 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Future Science Ltd |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT yonchevdimitar integratingcomputationalleadoptimizationdiagnosticswithanalogdesignandcandidateselection AT bajorathjurgen integratingcomputationalleadoptimizationdiagnosticswithanalogdesignandcandidateselection |