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https://hdl.handle.net/10316/111187
Título: | Artificial intelligence for prediction of biological activities and generation of molecular hits using stereochemical information | Autor: | Pereira, Tiago O. Abbasi, Maryam Oliveira, Rita I. Guedes, Romina A. Salvador, Jorge A. R. Arrais, Joel P. |
Palavras-chave: | Deep reinforcement learning; De novo drug design; Attention mechanism; Stereochemical information; Interpretability | Data: | Dez-2023 | Editora: | Springer Nature | Projeto: | UIDB/00326/2020 2021.151089.BD 2021.07538.BD CEECINST/00077/2021 |
Título da revista, periódico, livro ou evento: | Journal of Computer-Aided Molecular Design | Volume: | 37 | Número: | 12 | Resumo: | In this work, we develop a method for generating targeted hit compounds by applying deep reinforcement learning and attention mechanisms to predict binding affinity against a biological target while considering stereochemical information. The novelty of this work is a deep model Predictor that can establish the relationship between chemical structures and their corresponding [Formula: see text] values. We thoroughly study the effect of different molecular descriptors such as ECFP4, ECFP6, SMILES and RDKFingerprint. Also, we demonstrated the importance of attention mechanisms to capture long-range dependencies in molecular sequences. Due to the importance of stereochemical information for the binding mechanism, this information was employed both in the prediction and generation processes. To identify the most promising hits, we apply the self-adaptive multi-objective optimization strategy. Moreover, to ensure the existence of stereochemical information, we consider all the possible enumerated stereoisomers to provide the most appropriate 3D structures. We evaluated this approach against the Ubiquitin-Specific Protease 7 (USP7) by generating putative inhibitors for this target. The predictor with SMILES notations as descriptor plus bidirectional recurrent neural network using attention mechanism has the best performance. Additionally, our methodology identify the regions of the generated molecules that are important for the interaction with the receptor's active site. Also, the obtained results demonstrate that it is possible to discover synthesizable molecules with high biological affinity for the target, containing the indication of their optimal stereochemical conformation. | URI: | https://hdl.handle.net/10316/111187 | ISSN: | 0920-654X 1573-4951 |
DOI: | 10.1007/s10822-023-00539-9 | Direitos: | openAccess |
Aparece nas coleções: | I&D CNC - Artigos em Revistas Internacionais FFUC- Artigos em Revistas Internacionais I&D CISUC - Artigos em Revistas Internacionais |
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Artificial-intelligence-for-prediction-of-biological-activities-and-generation-of-molecular-hits-using-stereochemical-informationJournal-of-ComputerAided-Molecular-Design.pdf | 3.64 MB | Adobe PDF | Ver/Abrir |
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