PDF(Pubmed)
Abstract：
The battle against viral drug resistance highlights the need for innovative approaches to replace time-consuming and costly traditional methods. Deep generative models offer automation potential, especially in the fight against Human immunodeficiency virus (HIV), as they can synthesize diverse molecules effectively. In this paper, an application of an LSTM-based deep generative model named \"LSTM-ProGen\" is proposed to be tailored explicitly for the de novo design of drug candidate molecules that interact with a specific target protein (HIV-1 protease). LSTM-ProGen distinguishes itself by employing a long-short-term memory (LSTM) architecture, to generate novel molecules target specificity against the HIV-1 protease. Following a thorough training process involves fine-tuning LSTM-ProGen on a diverse range of compounds sourced from the ChEMBL database. The model was optimized to meet specific requirements, with multiple iterations to enhance its predictive capabilities and ensure it generates molecules that exhibit favorable target interactions. The training process encompasses an array of performance evaluation metrics, such as drug-likeness properties. Our evaluation includes extensive silico analysis using molecular docking and PCA-based visualization to explore the chemical space that the new molecules cover compared to those in the training set. These evaluations reveal that a subset of 12 de novo molecules generated by LSTM-ProGen exhibit a striking ability to interact with the target protein, rivaling or even surpassing the efficacy of native ligands. Extended versions with further refinement of LSTM-ProGen hold promise as versatile tools for designing efficacious and customized drug candidates tailored to specific targets, thus accelerating drug development and facilitating the discovery of new therapies for various diseases.
摘要：
与病毒耐药性的斗争凸显了对创新方法的需求，以取代耗时且昂贵的传统方法。深度生成模型提供自动化潜力，特别是在与人类免疫缺陷病毒（HIV）的斗争中，因为它们可以有效地合成不同的分子。在本文中,提出了一种基于LSTM的深度生成模型“LSTM-ProGen”的应用，该模型旨在明确地针对与特定靶蛋白（HIV-1蛋白酶）相互作用的药物候选分子的从头设计进行定制。LSTM-ProGen通过采用长短期记忆(LSTM)架构来区分自己，产生针对HIV-1蛋白酶的新分子靶特异性。经过全面的培训过程，包括对来自ChEMBL数据库的各种化合物进行微调LSTM-ProGen。该模型进行了优化，以满足特定的要求，进行多次迭代，以增强其预测能力，并确保其产生表现出有利的靶标相互作用的分子。培训过程包括一系列绩效评估指标，如药物相似特性。我们的评估包括使用分子对接和基于PCA的可视化进行广泛的硅分析，以探索新分子与训练集中的分子相比所覆盖的化学空间。这些评估表明，由LSTM-ProGen产生的12个从头分子的子集表现出与靶蛋白相互作用的惊人能力，与天然配体的功效相媲美甚至超越。进一步完善LSTM-ProGen的扩展版本有望成为设计针对特定目标的有效和定制候选药物的多功能工具。从而加速药物开发，促进发现各种疾病的新疗法。
