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Abstract：
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 contributionWe 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.
摘要：
在药物发现中，虚拟筛选对于识别潜在的命中化合物至关重要。本研究旨在提出一种新颖的管道，该管道采用机器学习模型，将各种常规筛选方法融合在一起。选择了一系列不同的蛋白质靶标，在使用四种不同的方法进行评分之前，对其相应的数据集进行了主动/诱饵分布分析：QSAR，药效团，对接,和2D形状相似性，最终被整合到一个共识分数中。微调的机器学习模型使用新颖的公式“w_new”进行排名计算了共识分数，并对每个目标进行富集研究。特别是,在PPARG和DPP4等特定蛋白质靶标方面,共识评分优于其他方法,AUC值分别为0.90和0.84.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,模型在R2值方面表现出中等到较高的性能.总之,这种新颖的工作流程始终如一地提供了卓越的结果，强调整体方法在药物发现中的重要性，其中定量指标和主动富集在确定最佳虚拟筛查方法中起着关键作用。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，合并多种方法以增强化合物选择。我们还引入了\'w_new\',一个开创性的指标，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，除了其他领域外，还彻底改变了他们在药物发现中的功效。
