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 \(pIC_{50}\) 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.

在这项工作中，我们开发了一种通过应用深度强化学习和注意机制来生成靶向命中化合物的方法，以预测与生物靶标的结合亲和力，同时考虑立体化学信息。这项工作的新颖之处在于一个深度模型预测器，它可以建立化学结构与其相应的\(pIC_{50}\)值之间的关系。我们深入研究了 ECFP4、ECFP6、SMILES 和 RDKFingerprint 等不同分子描述符的影响。此外，我们还证明了注意力机制对于捕获分子序列中的远程依赖性的重要性。由于立体化学信息对于结合机制的重要性，该信息被用于预测和生成过程。为了确定最有希望的命中，我们应用了自适应多目标优化策略。此外，为了确保立体化学信息的存在，我们考虑所有可能的枚举立体异构体以提供最合适的 3D 结构。我们通过针对泛素特异性蛋白酶 7 (USP7) 生成该靶点的假定抑制剂来评估该方法。以 SMILES 符号作为描述符加上使用注意力机制的双向循环神经网络的预测器具有最佳性能。此外，我们的方法确定了生成的分子中对于与受体活性位点相互作用很重要的区域。此外，获得的结果表明，有可能发现对靶标具有高生物亲和力的可合成分子，包含其最佳立体化学构象的指示。