New Methods and Tools for Computational Drug Discovery

项目摘要 我的目标是为药物发现开发有效和高效的计算方法，应用这些方法 方法fi寻找治疗疾病的新的和有效的fi药物，并将这些方法部署在简单易用的开放环境中 源工具。我的研究小组开创了深度神经网络的开发和集成 用于基于结构的药物设计的用户友好的分子对接软件，以预测其姿势和效力 与其分子靶标结合的小分子。我们将在我们的基础工作的基础上，使用深度 学会同时解决评分和抽样问题，这将克服可扩展性 现有方法固有的局限性。 我们建议开发基于结构的药物设计的fiFirst深度生成模型。不同于tra- 条件筛选、生成性建模并不局限于预置的fiNed化学空间。在生成模式下- ELING是一种深度神经网络，学习分子结构和性质的潜在分布 被表示为一个潜在的空间。新的结构可以从这个习得的潜在空间中提取出来 理想的特性。理想情况下，生成性模型将产生新的、接近最佳的分子结构。 几乎是在瞬间。我们假设使用现有的3D蛋白质和 配体结构将使我们能够创建通用模型，这些模型可以有效地应用于新的结构-- 由于蛋白质-配体相互作用的丰富性和普遍性，最终使靶标成为可能。我们将进一步 开发这些方法以支持生成优化的领导候选人，其中生成性 随着药物发现过程的进展，过程被更新，以包括实验分析的结果。 我们将继续应用我们的方法来鉴定分子间相互作用的小分子调节子。 与正常生理和疾病有关的问题。例如，使用我们当前的工具，我们识别了fi fiRST抑制前fi林-肌动蛋白相互作用，一个与癌症相关的抗血管生成靶点 糖尿病视网膜病变，我们计划进一步改进这些化合物，目的是鉴定康乃馨- 临床测试日期。我们将应用我们的方法来解决其他未被探索的分子目标， 例如NFATc2，它与癌症和自身免疫性疾病有关。这些潜在的应用 我们的方法将提供不偏不倚和现实的评估，进一步促进它们的发展。 最后，我们所有的代码和经过训练的深度神经网络模型将作为新工具部署 用于生成性建模或作为对我们广泛使用的用于计算药物的开源工具的增强 发现：(1)PHARMIT，用于基于结构的药物发现的交互式网络应用程序；(2)GNINA， 用于分子对接的C/C深度学习框架；以及(3)新发布的LIBMOLGRID，一个 与流行的深度学习工具包集成的加速分子网格的Python库。 这些工具和方法将使药物发现过程更容易获得和更有效。

Project Summary My goal is to develop effective and efficient computational methods for drug discovery, apply these methods to find new and efficacious drugs to treat diseases, and deploy these methods in easy-to-use open source tools. My research group pioneered the development and integration of deep neural networks in user-friendly molecular docking software for structure-based drug design to predict poses and potency of small molecules binding to their molecular targets. We will build on our foundational work by using deep learning to simultaneously solving the scoring and sampling problems, which will overcome scalability limitations inherent in current approaches. We propose to develop the first deep generative models for structure-based drug design. Unlike tra- ditional screening, generative modeling is not limited to a predefined chemical space. In generative mod- eling, a deep neural network learns an underlying distribution of molecular structures and properties represented as a latent space. New structures can be extracted from this learned latent space to have desirable properties. Ideally, a generative model will produce novel, near-optimal molecular structures almost instantaneously. We hypothesize that training generative models using existing 3D protein and ligand structures will allow us to create general models that can be productively applied to new, struc- turally enabled targets due to the richness and universality of protein-ligand interactions. We will further develop these methods to support the generation of optimized lead candidates, where the generative process is updated to include results from experimental assays as the drug discovery process progresses. We will continually apply our methods to identify small molecule modulators of molecular interac- tions relevant to normal physiology and disease. For example, using our current tools, we identified the first inhibitors of the profilin-actin interaction, an anti-angiogenesis target with relevance to cancer and diabetic retinopathy, and we plan to further improve these compounds with the goal of identifying candi- dates for clinical testing. We will apply our methods to address other under-explored molecular targets, such as NFATc2, which is implicated in cancer and autoimmune diseases. These prospective applications of our methods will provide unbiased and realistic evaluations that further inform their development. Finally, all of our code and trained deep neural network models will be deployed either as new tools for generative modeling or as enhancements to our widely used open source tools for computational drug discovery: (1) PHARMIT, an interactive web application for structure-based drug discovery; (2) GNINA, a C/C++ deep learning framework for molecular docking; and (3) the newly released LIBMOLGRID, a Python library for accelerated molecular gridding that integrates with popular deep learning toolkits. These tools and methods will make the drug discovery process more accessible and efficient.