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Aim: Generating a data and software infrastructure for evaluating multi-target compound (MT-CPD) design via deep generative modeling.
Methodology: The REINVENT 2.0 approach for generative modeling was extended for MT-CPD design and a large benchmark data set was curated.
Exemplary Results & Data: Proof-of-concept for deep generative MT-CPD design was established. Custom code and the benchmark set comprising 2809 MT-CPDs, 61,928 single-target and 295,395 inactive compounds from biological screens are made freely available.
Limitations & Next Steps: MT-CPD design via deep learning is still at its conceptual stages. It will be required to demonstrate experimental impact. The data and software we provide enable further investigation of MT-CPD design and generation of candidate molecules for experimental programs.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8147756 | PMC |
| http://dx.doi.org/10.2144/fsoa-2021-0033 | DOI Listing |
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Compound dataset and custom code for deep generative multi-target compound design.
Future Sci OA
April 2021
Department of Life Science Informatics & Data Science, B-IT, LIMES Program Unit Chemical Biology & Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, Bonn, D-53113, Germany.
Aim: Generating a data and software infrastructure for evaluating multi-target compound (MT-CPD) design via deep generative modeling.
Methodology: The REINVENT 2.0 approach for generative modeling was extended for MT-CPD design and a large benchmark data set was curated.
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