III:Small: Interpretable Deep Generative Models for Drug Development

III:Small：可解释的药物开发深度生成模型

• 批准号: 2133650
• 负责人: Xia Ning
• 金额: $ 50万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-11-01 至 2024-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2133650&HistoricalAwards=false
• 关键词: III; Small; Interpretable; Deep; Generative

Drug discovery is time-consuming and costly: it takes approximately 10-15 years and between $500 million to $2 billion to fully develop a new drug. Molecule optimization is a critical step in drug discovery to improve desired properties of drug candidates through chemical modification. For example, in lead (molecules showing both activity and selectivity towards a given target) optimization, the chemical structures of the lead molecules can be altered to improve their selectivity and specificity. Conventionally, this process is facilitated based on knowledge, intuition and experience of medicinal chemists, and is done via fragment-based screening or synthesis. Such an approach is not scalable. The objective of this project is to develop a new class of Artificial Intelligence (AI) methods and tools to conduct in silico molecule generation. Specifically, this project will focus on the following important aspects in AI-based in silico molecule optimization: 1) major scaffold retention, 2) molecule diversity, 3) molecule synthesizability; 4) multi-property optimization; and 5) interpretability. The central hypothesis underlying the proposed research is that the increasing amount of publicly available molecule data, including molecule properties, synthesis pathways and drug-likeness, contains a wealth of information that, if properly analyzed and utilized, can provide key insights in revealing, characterizing and automating the computational molecule generation and optimization process.Developing a new class of AI methods for in silico drug molecule optimization will require the development of novel AI models and methods for in silico molecule optimization. Examining designs based on new deep generative models, deep graph convolutional networks, conditional sampling approaches and reinforcement learning methods that learn from pairs of molecular graphs, and accordingly generate new molecular graphs with improved biochemical and biophysical properties, is necessary. The proposed research will also provide a holistic framework to explore prospective molecules that are sufficiently different from one another; and will investigate molecular graph search approaches and Bayesian optimization methods to guide search in the latent embedding (representation) space. For multi-property optimization, the proposed research will provide a pipeline structure and new reinforcement learning approaches. To understand and facilitate interpretable generative models, the proposed research will develop a set of novel methods including network dissection, perturbation-based attribution methods, self-explaining methods and disentanglement. This project will have substantial societal and educational impacts, and will enhance diversity in STEM through education and research dissemination. The broader scientific contributions of the will be the development of innovative AI methodologies and tools that will aid drug development. These technical innovations will not only address the key computational challenges in generative models for molecules, but also potentially generalize to other problems (e.g. cheminformatics, materials design) in which generation of structural data is highly needed and interpretation of such generation process is critical. The proposed research can potentially reduce the investment costs during drug discovery, increase its successful rate significantly, and ultimately aid in the improvement of the US health care quality.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

药物发现耗时且成本高昂：完全开发一种新药大约需要 10-15 年时间和 5 亿至 20 亿美元。分子优化是药物发现中的关键步骤，通过化学修饰来改善候选药物的所需特性。例如，在先导分子（对给定靶标显示活性和选择性的分子）优化中，可以改变先导分子的化学结构以提高其选择性和特异性。传统上，这一过程是基于药物化学家的知识、直觉和经验来促进的，并通过基于片段的筛选或合成来完成。这种方法不可扩展。该项目的目标是开发一类新型人工智能 (AI) 方法和工具来进行硅分子生成。具体来说，该项目将重点关注基于人工智能的计算机分子优化的以下重要方面：1）主要支架保留，2）分子多样性，3）分子可合成性； 4）多属性优化； 5）可解释性。拟议研究的中心假设是，越来越多的公开分子数据，包括分子特性、合成途径和药物相似性，包含大量信息，如果正确分析和利用这些信息，可以为揭示、表征和自动化计算分子生成和优化过程提供关键见解。开发用于计算机药物分子优化的新型人工智能方法将需要 开发用于计算机分子优化的新型人工智能模型和方法。有必要检查基于新的深度生成模型、深度图卷积网络、条件采样方法和强化学习方法的设计，这些方法从分子图对中学习，并相应地生成具有改进的生化和生物物理特性的新分子图。拟议的研究还将提供一个整体框架来探索彼此有很大差异的潜在分子；并将研究分子图搜索方法和贝叶斯优化方法来指导潜在嵌入（表示）空间中的搜索。对于多属性优化，所提出的研究将提供管道结构和新的强化学习方法。为了理解和促进可解释的生成模型，拟议的研究将开发一套新颖的方法，包括网络剖析、基于扰动的归因方法、自解释方法和解开。该项目将产生重大的社会和教育影响，并将通过教育和研究传播增强 STEM 的多样性。更广泛的科学贡献将是开发有助于药物开发的创新人工智能方法和工具。这些技术创新不仅将解决分子生成模型中的关键计算挑战，而且还可能推广到其他问题（例如化学信息学、材料设计），在这些问题中，结构数据的生成非常需要，并且对这种生成过程的解释至关重要。拟议的研究可能会降低药物发现过程中的投资成本，显着提高其成功率，并最终有助于提高美国医疗保健质量。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

A Deep Generative Model for Molecule Optimization via One Fragment Modification.

• DOI: 10.1038/s42256-021-00410-2
• 发表时间: 2021-12
• 期刊: Nature machine intelligence
• 影响因子: 23.8
• 作者: Chen Z;Min MR;Parthasarathy S;Ning X
• 通讯作者: Ning X

Using deep learning for the automated identification of cone and rod photoreceptors from adaptive optics imaging of the human retina

使用深度学习从人类视网膜的自适应光学成像中自动识别视锥细胞和杆状光感受器

• DOI: 10.1364/boe.470071
• 发表时间: 2022
• 期刊: Biomedical Optics Express
• 影响因子: 3.4
• 作者: Zhou, Mengxi;Doble, Nathan;Choi, Stacey S.;Jin, Tianyu;Xu, Chenwei;Parthasarathy, Srinivasan;Ramnath, Rajiv
• 通讯作者: Ramnath, Rajiv