FET:Medium: Drug discovery using quantum machine learning

FET：中：使用量子机器学习进行药物发现

• 批准号: 2210963
• 负责人: Swaroop Ghosh
• 金额: $ 120万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2210963&HistoricalAwards=false
• 关键词: FET; Medium; Drug; discovery; using

Existing drug discovery pipelines take 10-15 years from initial idea to market approval and cost billions of dollars. Extensive time is attributed to the expansive search space and lack of efficient search tools, whereas the cost is primarily attributed to inferior quality drug candidates that fail in clinical trials. High-quality search tools are required to increase the variety and quality of drug candidates that enter optimization. While high-performance computing assisted by Artificial Intelligence (AI) can screen a large pool of chemical compounds quickly to narrow down candidates that possess various desirable properties, a very large fraction of potential space for candidate drugs still goes unexplored. Furthermore, it is computationally expensive and inefficient in sampling the desired probability distributions in solution space which grows exponentially with the number of molecules. Quantum AI is more expressive, i.e., it can model a target probability distribution even with a limited number of qubits and parameters to sample from the unexplored regions of the search space. However, their true potential and application in drug discovery remain unexplored. This project will fill this void by creating Quantum Machine Learning (QML) models that will employ noisy quantum computers. If successful, this project will unleash new computational capabilities in discovery applications, e.g., by selecting novel lead chemical compounds versus important target proteins to treat diseases, such as cancer, by converging multiple disciplines. The generic and extendible QML toolset will enable the use of quantum computing for other discovery applications, e.g., material discovery. This project will advance quantum computing and quantum AI by addressing the scalability issue. It will develop an integrated introduction to quantum computing and application for K-12 teachers, including a professional development workshop and curricular materials that address local and national-level standards in science and engineering education. It will also develop undergraduate coursework supported by Penn State Quantum Minor program to prepare a quantum-ready workforce. Researchers will develop DrugVAE (a quantum variational autoencoder) to search and screen ligands and QDock (a quantum docking engine) to validate the ligands and aid in screening. Various scalability, application-level parallelization and training approaches for distributed computing will also be developed. Researchers will optimize and parallelize, map, and schedule the QML workloads from DrugVAE and QDock into target quantum computers considering architectural and hardware constraints for performance, resilience and cost. The output features will be provided to the classical neural network as needed. Researchers will computationally validate QML-generated compounds against slower, traditional docking as well as experimentally determined binding affinities. The research will provide materials for workforce development and undergraduate curriculum. Various tasks will be synergized through novel techniques, such as QML-specific optimization, target-specific search and refinement of model parameters, and optimization based on validation results. This project will cover all levels of abstractions to meet the end goal of drug discovery, e.g., program/circuit design, optimization, circuit-to-architecture mapping, parallelization, and scheduling.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.

现有的药物发现管道从最初的想法到获得市场批准需要 10 至 15 年的时间，耗资数十亿美元。大量的时间归因于广泛的搜索空间和缺乏有效的搜索工具，而成本主要归因于在临床试验中失败的劣质候选药物。需要高质量的搜索工具来增加进入优化的候选药物的种类和质量。虽然人工智能 (AI) 辅助的高性能计算可以快速筛选大量化合物，以缩小具有各种所需特性的候选药物的范围，但候选药物的很大一部分潜在空间仍未被开发。此外，在随分子数量呈指数增长的解空间中采样所需的概率分布时，计算成本昂贵且效率低下。量子人工智能更具表现力，即，即使使用有限数量的量子位和参数，它也可以对目标概率分布进行建模，以从搜索空间的未探索区域进行采样。然而，它们在药物发现中的真正潜力和应用仍有待探索。该项目将通过创建使用噪声量子计算机的量子机器学习（QML）模型来填补这一空白。如果成功，该项目将在发现应用中释放新的计算能力，例如，通过融合多个学科，选择新颖的先导化合物与重要的靶蛋白来治疗癌症等疾病。通用且可扩展的 QML 工具集将使量子计算能够用于其他发现应用，例如材料发现。该项目将通过解决可扩展性问题来推进量子计算和量子人工智能。它将为 K-12 教师开发量子计算和应用的综合介绍，包括专业发展研讨会和满足地方和国家级科学和工程教育标准的课程材料。它还将开发由宾夕法尼亚州立大学量子辅修项目支持的本科课程，以培养一支做好量子准备的劳动力队伍。研究人员将开发 DrugVAE（一种量子变分自动编码器）来搜索和筛选配体，并开发 QDock（一种量子对接引擎）来验证配体并帮助筛选。还将开发用于分布式计算的各种可扩展性、应用程序级并行化和培训方法。研究人员将考虑性能、弹性和成本的架构和硬件限制，优化和并行化、映射和调度来自 DrugVAE 和 QDock 的 QML 工作负载到目标量子计算机中。输出特征将根据需要提供给经典神经网络。研究人员将通过计算验证 QML 生成的化合物与较慢的传统对接以及实验确定的结合亲和力。该研究将为劳动力发展和本科课程提供材料。各种任务将通过新技术协同作用，例如特定于 QML 的优化、特定于目标的搜索和模型参数的细化以及基于验证结果的优化。该项目将涵盖实现药物发现最终目标的所有抽象层次，例如程序/电路设计、优化、电路到架构映射、并行化和调度。该奖项反映了 NSF 的法定使命，并被认为是值得的通过使用基金会的智力优势和更广泛的影响审查标准进行评估来提供支持。

项目成果

SCANN: Side Channel Analysis of Spiking Neural Networks

SCANN：尖峰神经网络的侧通道分析

• DOI: 10.3390/cryptography7020017
• 发表时间: 2023-06
• 期刊: Cryptography
• 影响因子: 1.6
• 作者: Nagarajan, Karthikeyan;Roy, Rupshali;Topaloglu, Rasit Onur;Kannan, Sachhidh;Ghosh, Swaroop
• 通讯作者: Ghosh, Swaroop

NeuralDock: Rapid and Conformation-Agnostic Docking of Small Molecules

NeuralDock：小分子的快速且与构象无关的对接

• DOI: 10.3389/fmolb.2022.867241
• 发表时间: 2022-03
• 期刊: Frontiers in Molecular Biosciences
• 影响因子: 5
• 作者: Sha, Congzhou M.;Wang, Jian;Dokholyan, Nikolay V.
• 通讯作者: Dokholyan, Nikolay V.

Shot Optimization in Quantum Machine Learning Architectures to Accelerate Training

量子机器学习架构中的镜头优化可加速训练

• DOI: 10.1109/access.2023.3270419
• 发表时间: 2023-01
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Phalak, Koustubh;Ghosh, Swaroop
• 通讯作者: Ghosh, Swaroop

Special Session: On the Reliability of Conventional and Quantum Neural Network Hardware

特别会议：论传统和量子神经网络硬件的可靠性

• DOI: 10.1109/vts52500.2021.9794194
• 发表时间: 2022-04
• 期刊: VLSI Test Symposium
• 作者: Sadi, Mehdi;He, Yi;Li, Yanjing;Alam, Mahabubul;Kundu, Satwik;Ghosh, Swaroop;Bahrami, Javad;Karimi, Naghmeh
• 通讯作者: Karimi, Naghmeh

Big versus small: The impact of aggregate size in disease

大与小：聚集体大小对疾病的影响

• DOI: 10.1002/pro.4686
• 发表时间: 2023-07
• 期刊: Protein Science : A Publication of the Protein Society
• 作者: Hnath, Brianna;Chen, Jiaxing;Reynolds, Joshua;Choi, Esther;Wang, Jian;Zhang, Dongyan;Sha, Congzhou M.;Dokholyan, Nikolay V.
• 通讯作者: Dokholyan, Nikolay V.