Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/111187
Title: Artificial intelligence for prediction of biological activities and generation of molecular hits using stereochemical information
Authors: Pereira, Tiago O. 
Abbasi, Maryam 
Oliveira, Rita I. 
Guedes, Romina A. 
Salvador, Jorge A. R. 
Arrais, Joel P. 
Keywords: Deep reinforcement learning; De novo drug design; Attention mechanism; Stereochemical information; Interpretability
Issue Date: Dec-2023
Publisher: Springer Nature
Project: Open access funding provided by FCT|FCCN (b-on). This work is funded by the FCT—Foundation for Science and Technology, I.P./MCTES through national funds (PIDDAC), within the scope of CISUC R &D Unit—UIDB/00326/2020. T.P. and R.I.O thanks FCT for funding the individual PhD Grants 2021.151089.BD and 2021.07538.BD, respectively. Maryam Abbasi thanks the National funding by FCT—Foundation for Science and Technology, P.I., through the institutional scientific employment program-contract (CEECINST/00077/2021). 
Serial title, monograph or event: Journal of Computer-Aided Molecular Design
Volume: 37
Issue: 12
Abstract: 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
Rights: openAccess
Appears in Collections:I&D CNC - Artigos em Revistas Internacionais
FFUC- Artigos em Revistas Internacionais
I&D CISUC - Artigos em Revistas Internacionais

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