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LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening
LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening
PulseAugur coverage of LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening — every cluster mentioning LIT-PCBA: An Unbiased Data Set for Machine Learning and Virtual Screening across labs, papers, and developer communities, ranked by signal.
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AI and ML methods show modest gains in virtual screening benchmarks
A new paper critically evaluates several AI-based docking tools, including DiffDock and GNINA, on the LIT-PCBA library. The study found that AutoDock-GPU combined with GNINA rescoring performed best among single methods…
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新的h-MINT模型改进了药物发现中的结合亲和力预测
研究人员开发了h-MINT，一种新颖的分层分子相互作用网络，旨在通过更好地表示分子片段来改进药物发现。该方法使用OverlapBPE标记化方法，允许片段重叠并捕获比传统原子级图更丰富的化学背景。h-MINT模型有效地模拟了原子和片段两个层面的相互作用，从而显著提高了结合亲和力预测和虚拟筛选的准确性。