Impacting Drug Discovery Programs with Large-Scale De Novo Design

JAN 25, 2024

Impacting Drug Discovery Programs with Large-Scale De Novo Design

Developing technologies to more comprehensively and effectively enable de novo design of high-quality chemical matter has been a long-standing goal of drug discovery.

To this end, Schrödinger has recently spearheaded the development of workflows that combine large-scale synthetically-aware de novo design methods (AutoDesigner) with rigorous free energy-based scoring methods (Active Learning FEP+) for potency and selectivity optimization of small molecules. Recent developments of this technology move beyond R-group design to core exploration, enabling its expanded application to early stage hit identification efforts and the discovery of back-up series.

In this webinar, we will describe several recent case studies from Schrödinger’s therapeutics group where these de novo design technologies have allowed teams to overcome critical design challenges and accelerate programs.

Highlights

  • Real-life comparison of AutoDesigner versus other common design methods, including an evaluation of chemical space explored, time spent, and ability to meet design goals
  • Design of novel cores during hit identification using AutoDesigner
  • Design of R-groups during hit-to-lead and lead optimization using AutoDesigner
  • Examples of improving potency and selectivity of a molecular glue and using de novo design to strengthen IP
  • Requirements and best practices to apply the technology to your drug discovery programs

Our Speakers

Pieter Bos, PhD

Principal Scientist II, Schrödinger

Pieter Bos is a principal scientist and product manager of AutoDesigner and De Novo Design workflows. At Schrödinger, his main focus is the research, development and optimization of automated compound design algorithms. Lead scientist for the design and execution of enumerated drug molecule libraries for internal and collaborative drug design projects. He received his Ph.D. in Synthetic Organic Chemistry from the University of Groningen in the laboratory of Prof. Ben Feringa. Prior to joining Schrödinger, he worked as a postdoctoral researcher in synthetic methodology development at Boston University (Prof. John Porco and Prof. Corey Stephenson) and small molecule drug discovery at Columbia University (Prof. Brent Stockwell.).

Sathesh Bhat, PhD

Executive Director, Schrödinger

Sathesh Bhat, Ph.D., executive director in the therapeutics group, joined Schrödinger in 2011. He is responsible for overseeing computational chemistry efforts on internal and partnered drug discovery programs at Schrödinger. Previously, Sathesh worked at both Merck and Eli Lilly leading computational efforts in several drug discovery programs. He obtained his Ph.D. from McGill University, which involved developing structure-based methods to predict binding free energies. Sathesh has co-authored multiple patents and publications and continues to publish on a wide variety of topics in computational chemistry.

Zef Könst, PhD

Principal Scientist II, Schrödinger

Zef Könst is a principal scientist in the therapeutics group at Schrödinger where he is responsible for drug discovery project execution as a medicinal chemist. Zef joined Schrödinger in 2020 after working at Novartis and Nurix Therapeutics and has contributed to four compounds in clinical development. He received his Ph.D. from University of California, Irvine under Professor Vanderwal.

Chinese Webinar: 薛定谔中文讲座:DLK在计算机辅助药物设计中的案例研究 ,网络讲座录制 计算机驱动用于治疗神经退行性疾病的高效、高选择性和穿透脑血屏障的DLK抑制剂的发现

DEC 20, 2024

Chinese Webinar: 薛定谔中文讲座:DLK在计算机辅助药物设计中的案例研究 ,网络讲座录制 计算机驱动用于治疗神经退行性疾病的高效、高选择性和穿透脑血屏障的DLK抑制剂的发现

Speaker

聂喆博士 | Schrödinger Therapeutics Group

Abstract

双亮氨酸拉链激酶(DLK)(又名MAP3K12)是混合系谱激酶(MLK)家族的成员,它包含一个N-末端激酶结构域,后面跟着两个亮氨酸拉链结构域以及一个富含甘氨酸/丝氨酸/脯氨酸的C-末端结构域。它主要在神经元细胞中表达,特别是在神经元的突触末端和轴突中。神经元受损或其他细胞应激导致DLK二聚化、自磷酸化、MKK7的磷酸化和JNK信号通路的激活。最近的研究表明,遗传缺失或药物抑制DLK会减轻阿尔茨海默病和肌萎缩性脊髓侧索硬化症(ALS)模型中的突触丢失、神经细胞退化和功能下降,因此DLK成为治疗神经退行性疾病的热门治疗靶点。

项目借助Schrödinger的自由能微扰(FEP+)技术来预测化合物与hDLK的结合亲和力,并优先考虑由我们的专有枚举算法AutoDesigner生成或手绘的设计思路。在苗头化合物发现(Hit-Finding)阶段,FEP+以最小的铰链结合片段为出发点,通过分析现有的DLK共晶结构,对单环和双环核心进行建模和评估。合成了一组具有良好药效预测的配体,从而在项目启动的前2个月内鉴定出多个新颖的、纳摩尔级的起始点。通过对顶级核心的R基团枚举进行进一步的构效关系(SAR)探索,可以识别最有希望的先导系列以及多个备用组。在先导化合物优化(Lead Optimization)阶段,结合FEP+技术和预测性的ADMET建模工具,包括我们最近发布的用于预测脑渗透性Kp,uu的溶解能量方法 (E-sol),团队建立了一种有效的工作流程,以应对多个挑战,如中枢神经系统穿透、hERG抑制、细胞毒性和选择性。最终,团队鉴定出了高效、选择性和穿透脑血脑屏障的双亮氨酸拉链激酶(DLK)抑制剂,这些抑制剂在离体轴突断裂实验中表现出神经保护性,并在体内小鼠小脑帕金森病模型中显示出剂量依赖性的p-c-Jun减少。

4th Future Battery Forum

Conference

4th Future Battery Forum

CalendarDate & Time
  • November 27th-28th, 2023
LocationLocation
  • Berlin, Germany

Schrödinger is excited to be participating in the 4th Future Battery Forum taking place on November 27th-28th in Berlin, Germany. Visit us at our booth to gain hands-on experience with Schrödinger software and meet our expert computational scientists.