DNA-Encoded Library (DEL) technology has enabled significant advances in hit identification by enabling efficient testing of combinatorially-generated molecular libraries. DEL screens measure protein binding affinity though sequencing reads of molecules tagged with unique DNA-barcodes that survive a series of selection experiments. Computational models have been deployed to learn the latent binding affinities that are correlated to the sequenced count data; however, this correlation is often obfuscated by various sources of noise introduced in its complicated data-generation process. In order to denoise DEL count data and screen for molecules with good binding affinity, computational models require the correct assumptions in their modeling structure to capture the correct signals underlying the data. Recent advances in DEL models have focused on probabilistic formulations of count data, but existing approaches have thus far been limited to only utilizing 2-D molecule-level representations. We introduce a new paradigm, DEL-Dock, that combines ligand-based descriptors with 3-D spatial information from docked protein-ligand complexes. 3-D spatial information allows our model to learn over the actual binding modality rather than using only structured-based information of the ligand. We show that our model is capable of effectively denoising DEL count data to predict molecule enrichment scores that are better correlated with experimental binding affinity measurements compared to prior works. Moreover, by learning over a collection of docked poses we demonstrate that our model, trained only on DEL data, implicitly learns to perform good docking pose selection without requiring external supervision from expensive-to-source protein crystal structures.
Molecular Embedding-Based Algorithm Selection in Protein-Ligand Docking
Division of Natural and Applied Sciences, Duke Kunshan University（杜克昆山大学自然科学与应用科学系）；Machine Learning Department, Mohamed bin Zayed University of Artificial Intelligence（莫扎德人工智能大学机器学习系）
Hong Kong Baptist University（香港浸会大学）；Shanghai AI Laboratory（上海人工智能实验室）；Huazhong University of Science and Technology（华中科技大学）；Shanghai Jiao Tong University（上海交通大学）；Microsoft Research AI4Science（微软研究院AI科学部）
SkeleDock: A Web Application for Scaffold Docking in PlayMolecule
SkeleDock是一种支架对接算法，其使用蛋白质-配体复合物的结构作为模板来模拟化学相似系统的结合模式。该算法在D3 R Grand Challenge 4 pose prediction challenge中进行了评估，并取得了有竞争力的性能。此外，我们表明，如果目标配体的结晶片段是可用的，SkeleDock可以优于rDock对接软件在预测的结合模式。本文还讨论了该算法的能力，以模拟大环和处理支架跳跃。SkeleDock可以在https://playmolecule.org/SkeleDock/上访问。
