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The novelty of this work is a deep model Predictor that can establish the relationship between chemical structures and their corresponding <jats:inline-formula><jats:alternatives><jats:tex-math>$$pIC_{50}$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                  <mml:mrow>\n                    <mml:mi>p<\/mml:mi>\n                    <mml:mi>I<\/mml:mi>\n                    <mml:msub>\n                      <mml:mi>C<\/mml:mi>\n                      <mml:mn>50<\/mml:mn>\n                    <\/mml:msub>\n                  <\/mml:mrow>\n                <\/mml:math><\/jats:alternatives><\/jats:inline-formula> 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\u2019s 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.<\/jats:p>","DOI":"10.1007\/s10822-023-00539-9","type":"journal-article","created":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T14:02:16Z","timestamp":1697551336000},"page":"791-806","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Artificial intelligence for prediction of biological activities and generation of molecular hits using stereochemical information"],"prefix":"10.1007","volume":"37","author":[{"given":"Tiago O.","family":"Pereira","sequence":"first","affiliation":[]},{"given":"Maryam","family":"Abbasi","sequence":"additional","affiliation":[]},{"given":"Rita I.","family":"Oliveira","sequence":"additional","affiliation":[]},{"given":"Romina A.","family":"Guedes","sequence":"additional","affiliation":[]},{"given":"Jorge A. 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