In drug discovery, virtual screening is crucial for identifying potential hit compounds. This study aims to present a novel pipeline that employs machine learning models that amalgamates various conventional screening methods. A diverse array of protein targets was selected, and their corresponding datasets were subjected to active/decoy distribution analysis prior to scoring using four distinct methods: QSAR, Pharmacophore, docking, and 2D shape similarity, which were ultimately integrated into a single consensus score. The fine-tuned machine learning models were ranked using the novel formula “w_new”, consensus scores were calculated, and an enrichment study was performed for each target. Distinctively, consensus scoring outperformed other methods in specific protein targets such as PPARG and DPP4, achieving AUC values of 0.90 and 0.84, respectively. Remarkably, this approach consistently prioritized compounds with higher experimental PIC50 values compared to all other screening methodologies. Moreover, the models demonstrated a range of moderate to high performance in terms of R2 values during external validation. In conclusion, this novel workflow consistently delivered superior results, emphasizing the significance of a holistic approach in drug discovery, where both quantitative metrics and active enrichment play pivotal roles in identifying the best virtual screening methodology. Scientific contribution We presented a novel consensus scoring workflow in virtual screening, merging diverse methods for enhanced compound selection. We also introduced ‘w_new’, a groundbreaking metric that intricately refines machine learning model rankings by weighing various model-specific parameters, revolutionizing their efficacy in drug discovery in addition to other domains.

中文翻译：

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药物发现的共识整体虚拟筛选：一种新颖的机器学习模型方法


在药物发现中，虚拟筛选对于识别潜在的热门化合物至关重要。这项研究旨在提出一种采用机器学习模型的新颖管道，该模型融合了各种传统的筛选方法。选择了多种蛋白质靶标，对其相应的数据集进行了活性/诱饵分布分析，然后使用四种不同的方法进行评分：QSAR、药效团、对接和 2D 形状相似性，最终将其整合为单一共识评分。使用新颖的公式“w_new”对微调的机器学习模型进行排名，计算共识分数，并对每个目标进行丰富研究。值得注意的是，共识评分在特定蛋白质靶标（例如 PPARG 和 DPP4）方面优于其他方法，分别达到 0.90 和 0.84 的 AUC 值。值得注意的是，与所有其他筛选方法相比，这种方法始终优先考虑具有较高实验 PIC50 值的化合物。此外，在外部验证过程中，这些模型在 R2 值方面表现出了一系列中等至高性能的性能。总之，这种新颖的工作流程始终如一地提供了卓越的结果，强调了药物发现中整体方法的重要性，其中定量指标和主动富集在确定最佳虚拟筛选方法方面发挥着关键作用。科学贡献我们在虚拟筛选中提出了一种新颖的共识评分工作流程，融合了多种方法以增强化合物选择。我们还引入了“w_new”，这是一个突破性的指标，通过权衡各种特定于模型的参数来复杂地完善机器学习模型排名，彻底改变其在药物发现以及其他领域的功效。