McDonald Matthew A, Koscher Brent A, Canty Richard B, Zhang Jason, Ning Angelina, Jensen Klavs F
Massachusetts Institute of Technology, Department of Chemical Engineering, 77 Massachusetts Avenue, Cambridge, Massachusetts 02139, United States.
Drexel University, Department of Chemical and Biological Engineering, 3101 Ludlow St, Philadelphia, Pennsylvania 19104, United States.
ACS Cent Sci. 2025 Feb 5;11(2):346-356. doi: 10.1021/acscentsci.4c01991. eCollection 2025 Feb 26.
Different experiments of differing fidelities are commonly used in the search for new drug molecules. In classic experimental funnels, libraries of molecules undergo sequential rounds of virtual, coarse, and refined experimental screenings, with each level balanced between the cost of experiments and the number of molecules screened. Bayesian optimization offers an alternative approach, using iterative experiments to locate optimal molecules with fewer experiments than large-scale screening, but without the ability to weigh the costs and benefits of different types of experiments. In this work, we combine the multifidelity approach of the experimental funnel with Bayesian optimization to search for drug molecules iteratively, taking full advantage of different types of experiments, their costs, and the quality of the data they produce. We first demonstrate the utility of the multifidelity Bayesian optimization (MF-BO) approach on a series of drug targets with data reported in ChEMBL, emphasizing what properties of the chemical search space result in substantial acceleration with MF-BO. Then we integrate the MF-BO experiment selection algorithm into an autonomous molecular discovery platform to illustrate the prospective search for new histone deacetylase inhibitors using docking scores, single-point percent inhibitions, and dose-response IC values as low-, medium-, and high-fidelity experiments. A chemical search space with appropriate diversity and fidelity correlation for use with MF-BO was constructed with a genetic generative algorithm. The MF-BO integrated platform then docked more than 3,500 molecules, automatically synthesized and screened more than 120 molecules for percent inhibition, and selected a handful of molecules for manual evaluation at the highest fidelity. Many of the molecules screened have never been reported in any capacity. At the end of the search, several new histone deacetylase inhibitors were found with submicromolar inhibition, free of problematic hydroxamate moieties that constrain the use of current inhibitors.
在寻找新的药物分子时，通常会使用不同保真度的不同实验。在经典的实验流程中，分子库会经历虚拟、粗粒度和精细实验筛选的连续轮次，每一层都在实验成本和筛选的分子数量之间取得平衡。贝叶斯优化提供了一种替代方法，通过迭代实验来定位最优分子，与大规模筛选相比所需实验更少，但无法权衡不同类型实验的成本和收益。在这项工作中，我们将实验流程的多保真度方法与贝叶斯优化相结合，以迭代方式搜索药物分子，充分利用不同类型的实验、它们的成本以及所产生数据的质量。我们首先在ChEMBL中报告的数据的一系列药物靶点上展示了多保真度贝叶斯优化（MF-BO）方法的效用，强调了化学搜索空间的哪些属性会导致MF-BO带来显著加速。然后，我们将MF-BO实验选择算法集成到一个自主分子发现平台中，以说明使用对接分数、单点抑制百分比和剂量反应IC值作为低、中、高保真度实验来前瞻性地搜索新的组蛋白去乙酰化酶抑制剂。使用遗传生成算法构建了一个与MF-BO一起使用的具有适当多样性和保真度相关性的化学搜索空间。MF-BO集成平台随后对接了3500多个分子，自动合成并筛选了120多个分子的抑制百分比，并选择了少数分子进行最高保真度的人工评估。筛选出的许多分子以前从未有过任何报道。在搜索结束时，发现了几种新的组蛋白去乙酰化酶抑制剂，其抑制作用在亚微摩尔级别，且没有限制当前抑制剂使用的有问题的异羟肟酸基团。
