1
|
Futran AS, Lu T, Amberg-Johnson K, Xu J, Yang X, He S, Boyce S, Bell JA, Pelletier R, Suzuki T, Huang X, Qian H, Fang L, Xing L, Xu Z, Kurtz SE, Tyner JW, Tang W, Guo T, Akinsanya K, Madge D, Jensen KK. Ubiquitin-specific protease 7 inhibitors reveal a differentiated mechanism of p53-driven anti-cancer activity. iScience 2024; 27:109693. [PMID: 38689642 PMCID: PMC11059122 DOI: 10.1016/j.isci.2024.109693] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 01/11/2024] [Accepted: 04/05/2024] [Indexed: 05/02/2024] Open
Abstract
The USP7 deubiquitinase regulates proteins involved in the cell cycle, DNA repair, and epigenetics and has been implicated in cancer progression. USP7 inhibition has been pursued for the development of anti-cancer therapies. Here, we describe the discovery of potent and specific USP7 inhibitors exemplified by FX1-5303. FX1-5303 was used as a chemical probe to study the USP7-mediated regulation of p53 signaling in cells. It demonstrates mechanistic differences compared to MDM2 antagonists, a related class of anti-tumor agents that act along the same pathway. FX1-5303 synergizes with the clinically approved BCL2 inhibitor venetoclax in acute myeloid leukemia (AML) cell lines and ex vivo patient samples and leads to strong tumor growth inhibition in in vivo mouse xenograft models of multiple myeloma and AML. This work introduces new USP7 inhibitors, differentiates their mechanism of action from MDM2 inhibition, and identifies specific opportunities for their use in the treatment of AML.
Collapse
Affiliation(s)
- Alan S. Futran
- Schrödinger, 1540 Broadway 24th Floor, New York, NY, USA
| | - Tao Lu
- WuXi AppTec, 288 Fute Zhong Road, Waigaoqiao Free Trade Zone, Shanghai 200131, China
| | | | - Jiayi Xu
- Schrödinger, 1540 Broadway 24th Floor, New York, NY, USA
| | - Xiaoxiao Yang
- WuXi AppTec, 288 Fute Zhong Road, Waigaoqiao Free Trade Zone, Shanghai 200131, China
| | - Saidi He
- WuXi AppTec, 288 Fute Zhong Road, Waigaoqiao Free Trade Zone, Shanghai 200131, China
| | - Sarah Boyce
- Schrödinger, 1540 Broadway 24th Floor, New York, NY, USA
| | | | | | - Takao Suzuki
- WuXi AppTec, 288 Fute Zhong Road, Waigaoqiao Free Trade Zone, Shanghai 200131, China
| | - Xianhai Huang
- Schrödinger, 1540 Broadway 24th Floor, New York, NY, USA
| | - Heng Qian
- WuXi AppTec, 288 Fute Zhong Road, Waigaoqiao Free Trade Zone, Shanghai 200131, China
| | - Liping Fang
- WuXi AppTec, 288 Fute Zhong Road, Waigaoqiao Free Trade Zone, Shanghai 200131, China
| | - Li Xing
- WuXi AppTec, 288 Fute Zhong Road, Waigaoqiao Free Trade Zone, Shanghai 200131, China
| | - Zhaowu Xu
- WuXi AppTec, 288 Fute Zhong Road, Waigaoqiao Free Trade Zone, Shanghai 200131, China
| | - Stephen E. Kurtz
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Division of Hematology & Medical Oncology, Department of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Jeffrey W. Tyner
- Knight Cancer Institute, Oregon Health & Science University, Portland, OR, USA
- Department of Cell, Developmental & Cancer Biology, Oregon Health & Science University, Portland, OR, USA
| | - Wayne Tang
- Schrödinger, 1540 Broadway 24th Floor, New York, NY, USA
| | - Tao Guo
- WuXi AppTec, 288 Fute Zhong Road, Waigaoqiao Free Trade Zone, Shanghai 200131, China
| | | | - David Madge
- WuXi AppTec, 288 Fute Zhong Road, Waigaoqiao Free Trade Zone, Shanghai 200131, China
| | | |
Collapse
|
2
|
Frasnetti E, Magni A, Castelli M, Serapian SA, Moroni E, Colombo G. Structures, dynamics, complexes, and functions: From classic computation to artificial intelligence. Curr Opin Struct Biol 2024; 87:102835. [PMID: 38744148 DOI: 10.1016/j.sbi.2024.102835] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Revised: 04/14/2024] [Accepted: 04/22/2024] [Indexed: 05/16/2024]
Abstract
Computational approaches can provide highly detailed insight into the molecular recognition processes that underlie drug binding, the assembly of protein complexes, and the regulation of biological functional processes. Classical simulation methods can bridge a wide range of length- and time-scales typically involved in such processes. Lately, automated learning and artificial intelligence methods have shown the potential to expand the reach of physics-based approaches, ushering in the possibility to model and even design complex protein architectures. The synergy between atomistic simulations and AI methods is an emerging frontier with a huge potential for advances in structural biology. Herein, we explore various examples and frameworks for these approaches, providing select instances and applications that illustrate their impact on fundamental biomolecular problems.
Collapse
Affiliation(s)
- Elena Frasnetti
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy
| | - Andrea Magni
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy
| | - Matteo Castelli
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy
| | - Stefano A Serapian
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy
| | | | - Giorgio Colombo
- Department of Chemistry, University of Pavia, via Taramelli 12, 27100 Pavia, Italy.
| |
Collapse
|
3
|
Carney DW, Leffler AE, Bell JA, Chandrasinghe AS, Cheng C, Chang E, Dornford A, Dougan DR, Frye LL, Grimes ME, Knehans T, Knight JL, Komandla M, Lane W, Li H, Newman SR, Phimister K, Saikatendu KS, Silverstein H, Vafaei S. Exploiting high-energy hydration sites for the discovery of potent peptide aldehyde inhibitors of the SARS-CoV-2 main protease with cellular antiviral activity. Bioorg Med Chem 2024; 103:117577. [PMID: 38518735 DOI: 10.1016/j.bmc.2023.117577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 03/24/2024]
Abstract
Small-molecule antivirals that prevent the replication of the SARS-CoV-2 virus by blocking the enzymatic activity of its main protease (Mpro) are and will be a tenet of pandemic preparedness. However, the peptidic nature of such compounds often precludes the design of compounds within favorable physical property ranges, limiting cellular activity. Here we describe the discovery of peptide aldehyde Mpro inhibitors with potent enzymatic and cellular antiviral activity. This structure-activity relationship (SAR) exploration was guided by the use of calculated hydration site thermodynamic maps (WaterMap) to drive potency via displacement of waters from high-energy sites. Thousands of diverse compounds were designed to target these high-energy hydration sites and then prioritized for synthesis by physics- and structure-based Free-Energy Perturbation (FEP+) simulations, which accurately predicted biochemical potencies. This approach ultimately led to the rapid discovery of lead compounds with unique SAR that exhibited potent enzymatic and cellular activity with excellent pan-coronavirus coverage.
Collapse
Affiliation(s)
- Daniel W Carney
- Takeda Development Center Americas, Inc, 9625 Towne Centre Drive, San Diego, CA 92121, United States.
| | - Abba E Leffler
- Schrödinger, Inc, 1540 Broadway, New York, NY 10036, United States.
| | - Jeffrey A Bell
- Schrödinger, Inc, 1540 Broadway, New York, NY 10036, United States
| | | | - Cecilia Cheng
- Schrödinger, Inc, 9868 Scranton Road, Suite 3200, San Diego, CA 92121, United States
| | - Edcon Chang
- Takeda Development Center Americas, Inc, 9625 Towne Centre Drive, San Diego, CA 92121, United States
| | - Adam Dornford
- Schrödinger, Inc, 1 Main St, 11th Floor, Cambridge, MA 02142, United States
| | - Douglas R Dougan
- Takeda Development Center Americas, Inc, 9625 Towne Centre Drive, San Diego, CA 92121, United States
| | - Leah L Frye
- Schrödinger, Inc, 101 SW Main Street, Suite 1300, Portland, OR 97204, United States
| | - Mary E Grimes
- Schrödinger, Inc, 101 SW Main Street, Suite 1300, Portland, OR 97204, United States
| | - Tim Knehans
- Schrödinger GmbH, Glücksteinallee 25, 68163 Mannheim, Germany
| | | | - Mallareddy Komandla
- Takeda Development Center Americas, Inc, 9625 Towne Centre Drive, San Diego, CA 92121, United States
| | - Weston Lane
- Treeline Biosciences, 500 Arsenal Way, Watertown, MA 02472, United States
| | - Hubert Li
- Schrödinger, Inc, 9868 Scranton Road, Suite 3200, San Diego, CA 92121, United States
| | - Sophia R Newman
- Schrödinger, Inc, 101 SW Main Street, Suite 1300, Portland, OR 97204, United States
| | - Katalin Phimister
- Schrödinger Technologies Limited, 1st Floor West, Davidson House, Forbury Square, Reading RG1 3EU, United Kingdom
| | - Kumar S Saikatendu
- Takeda Development Center Americas, Inc, 9625 Towne Centre Drive, San Diego, CA 92121, United States
| | - Hercules Silverstein
- Schrödinger, Inc, 101 SW Main Street, Suite 1300, Portland, OR 97204, United States
| | | |
Collapse
|
4
|
Powers A, Yu HH, Suriana P, Koodli RV, Lu T, Paggi JM, Dror RO. Geometric Deep Learning for Structure-Based Ligand Design. ACS CENTRAL SCIENCE 2023; 9:2257-2267. [PMID: 38161364 PMCID: PMC10755842 DOI: 10.1021/acscentsci.3c00572] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Revised: 10/26/2023] [Accepted: 10/27/2023] [Indexed: 01/03/2024]
Abstract
A pervasive challenge in drug design is determining how to expand a ligand-a small molecule that binds to a target biomolecule-in order to improve various properties of the ligand. Adding single chemical groups, known as fragments, is important for lead optimization tasks, and adding multiple fragments is critical for fragment-based drug design. We have developed a comprehensive framework that uses machine learning and three-dimensional protein-ligand structures to address this challenge. Our method, FRAME, iteratively determines where on a ligand to add fragments, selects fragments to add, and predicts the geometry of the added fragments. On a comprehensive benchmark, FRAME consistently improves predicted affinity and selectivity relative to the initial ligand, while generating molecules with more drug-like chemical properties than docking-based methods currently in widespread use. FRAME learns to accurately describe molecular interactions despite being given no prior information on such interactions. The resulting framework for quality molecular hypothesis generation can be easily incorporated into the workflows of medicinal chemists for diverse tasks, including lead optimization, fragment-based drug discovery, and de novo drug design.
Collapse
Affiliation(s)
- Alexander
S. Powers
- Department
of Chemistry, Stanford University, Stanford, California 94305, United States
- Department
of Computer Science, Stanford University, Stanford, California 94305, United States
- Department
of Molecular and Cellular Physiology, Stanford
University School of Medicine, Stanford, California 94305, United States
- Department
of Structural Biology, Stanford University
School of Medicine, Stanford, California 94305, United States
- Institute
for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305, United States
| | - Helen H. Yu
- Department
of Computer Science, Stanford University, Stanford, California 94305, United States
- Department
of Molecular and Cellular Physiology, Stanford
University School of Medicine, Stanford, California 94305, United States
- Department
of Structural Biology, Stanford University
School of Medicine, Stanford, California 94305, United States
- Institute
for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305, United States
| | - Patricia Suriana
- Department
of Computer Science, Stanford University, Stanford, California 94305, United States
- Department
of Molecular and Cellular Physiology, Stanford
University School of Medicine, Stanford, California 94305, United States
- Department
of Structural Biology, Stanford University
School of Medicine, Stanford, California 94305, United States
- Institute
for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305, United States
| | - Rohan V. Koodli
- Department
of Computer Science, Stanford University, Stanford, California 94305, United States
- Department
of Molecular and Cellular Physiology, Stanford
University School of Medicine, Stanford, California 94305, United States
- Department
of Structural Biology, Stanford University
School of Medicine, Stanford, California 94305, United States
- Institute
for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305, United States
- Biomedical
Informatics Program, Stanford University
School of Medicine, Stanford, California 94305, United States
| | - Tianyu Lu
- Department
of Computer Science, Stanford University, Stanford, California 94305, United States
- Department
of Molecular and Cellular Physiology, Stanford
University School of Medicine, Stanford, California 94305, United States
- Department
of Structural Biology, Stanford University
School of Medicine, Stanford, California 94305, United States
- Institute
for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305, United States
- Department
of Bioengineering, Stanford University, Stanford, California 94305, United States
| | - Joseph M. Paggi
- Department
of Computer Science, Stanford University, Stanford, California 94305, United States
- Department
of Molecular and Cellular Physiology, Stanford
University School of Medicine, Stanford, California 94305, United States
- Department
of Structural Biology, Stanford University
School of Medicine, Stanford, California 94305, United States
- Institute
for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305, United States
| | - Ron O. Dror
- Department
of Computer Science, Stanford University, Stanford, California 94305, United States
- Department
of Molecular and Cellular Physiology, Stanford
University School of Medicine, Stanford, California 94305, United States
- Department
of Structural Biology, Stanford University
School of Medicine, Stanford, California 94305, United States
- Institute
for Computational and Mathematical Engineering, Stanford University, Stanford, California 94305, United States
| |
Collapse
|
5
|
de Oliveira C, Leswing K, Feng S, Kanters R, Abel R, Bhat S. FEP Protocol Builder: Optimization of Free Energy Perturbation Protocols Using Active Learning. J Chem Inf Model 2023; 63:5592-5603. [PMID: 37594480 DOI: 10.1021/acs.jcim.3c00681] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/19/2023]
Abstract
Significant improvements have been made in the past decade to methods that rapidly and accurately predict binding affinity through free energy perturbation (FEP) calculations. This has been driven by recent advances in small-molecule force fields and sampling algorithms combined with the availability of low-cost parallel computing. Predictive accuracies of ∼1 kcal mol-1 have been regularly achieved, which are sufficient to drive potency optimization in modern drug discovery campaigns. Despite the robustness of these FEP approaches across multiple target classes, there are invariably target systems that do not display expected performance with default FEP settings. Traditionally, these systems required labor-intensive manual protocol development to arrive at parameter settings that produce a predictive FEP model. Due to the (a) relatively large parameter space to be explored, (b) significant compute requirements, and (c) limited understanding of how combinations of parameters can affect FEP performance, manual FEP protocol optimization can take weeks to months to complete, and often does not involve rigorous train-test set splits, resulting in potential overfitting. These manual FEP protocol development timelines do not coincide with tight drug discovery project timelines, essentially preventing the use of FEP calculations for these target systems. Here, we describe an automated workflow termed FEP Protocol Builder (FEP-PB) to rapidly generate accurate FEP protocols for systems that do not perform well with default settings. FEP-PB uses an active-learning workflow to iteratively search the protocol parameter space to develop accurate FEP protocols. To validate this approach, we applied it to pharmaceutically relevant systems where default FEP settings could not produce predictive models. We demonstrate that FEP-PB can rapidly generate accurate FEP protocols for the previously challenging MCL1 system with limited human intervention. We also apply FEP-PB in a real-world drug discovery setting to generate an accurate FEP protocol for the p97 system. FEP-PB is able to generate a more accurate protocol than the expert user, rapidly validating p97 as amenable to free energy calculations. Additionally, through the active-learning workflow, we are able to gain insight into which parameters are most important for a given system. These results suggest that FEP-PB is a robust tool that can aid in rapidly developing accurate FEP protocols and increasing the number of targets that are amenable to the technology.
Collapse
Affiliation(s)
- César de Oliveira
- Schrodinger, Inc., 9868 Scranton Road, Suite 3200, San Diego, California 92121, United States
| | - Karl Leswing
- Schrodinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Shulu Feng
- Schrodinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - René Kanters
- Schrodinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Robert Abel
- Schrodinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| | - Sathesh Bhat
- Schrodinger, Inc., 1540 Broadway, 24th Floor, New York, New York 10036, United States
| |
Collapse
|
6
|
Bourougaa L, Ouassaf M, Shtaiwi A. Discovery of novel potent drugs for influenza by inhibiting the vital function of neuraminidase via fragment-based drug design (FBDD) and molecular dynamics simulation strategies. J Biomol Struct Dyn 2023:1-15. [PMID: 37640004 DOI: 10.1080/07391102.2023.2251065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 08/17/2023] [Indexed: 08/31/2023]
Abstract
The current work describes a fragment linking methodology to generate new neuraminidase inhibitors. A total number of 28,977 fragments from Zinc 20 have been obtained and screened for neuraminidase receptor affinity. Using Schrödinger software, the highest-scoring 270 fragment hits (with scores greater than -7.6) were subjected to fragment combining to create 100 new molecules. These 100 novel compounds were studied using XP docking to evaluate the molecular interaction modes and their binding affinity to neuraminidase receptor. The top ten molecules were selected, for ADMET, drug-likeness features. Based on these characteristics, the best four developed molecules and Zanamivir were submitted to a molecular dynamics simulation investigation to estimate their dynamics within the neuraminidase receptor using Gromacs software. All MD simulation findings show that the generated complexes are very stable when compared to the clinical inhibitor (Zanamivir). In addition, the four designed neuraminidase inhibitors formed very stable complexes with neuraminidase receptor (with total binding energies ranging from -83.50 to -107.85 Kj/mol) according to the total binding energy calculated by MM-PBSA. For the objective of developing new influenza medications, these novel molecules have the potential to be further evaluated in vitro and in vivo for influenza drug discovery.Communicated by Ramaswamy H. Sarma.
Collapse
Affiliation(s)
- Lotfi Bourougaa
- Group of Computational and Medicinal Chemistry, Laboratory of Molecular Chemistry and Environment, University of Biskra, Biskra, Algeria
| | - Mebarka Ouassaf
- Group of Computational and Medicinal Chemistry, Laboratory of Molecular Chemistry and Environment, University of Biskra, Biskra, Algeria
| | - Amneh Shtaiwi
- Faculty of Pharmacy, Middle East University Amman, Amman, Jordan
| |
Collapse
|
7
|
Ivanenkov Y, Zagribelnyy B, Malyshev A, Evteev S, Terentiev V, Kamya P, Bezrukov D, Aliper A, Ren F, Zhavoronkov A. The Hitchhiker's Guide to Deep Learning Driven Generative Chemistry. ACS Med Chem Lett 2023; 14:901-915. [PMID: 37465301 PMCID: PMC10351082 DOI: 10.1021/acsmedchemlett.3c00041] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 06/09/2023] [Indexed: 07/20/2023] Open
Abstract
This microperspective covers the most recent research outcomes of artificial intelligence (AI) generated molecular structures from the point of view of the medicinal chemist. The main focus is on studies that include synthesis and experimental in vitro validation in biochemical assays of the generated molecular structures, where we analyze the reported structures' relevance in modern medicinal chemistry and their novelty. The authors believe that this review would be appreciated by medicinal chemistry and AI-driven drug design (AIDD) communities and can be adopted as a comprehensive approach for qualifying different research outcomes in AIDD.
Collapse
Affiliation(s)
- Yan Ivanenkov
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Bogdan Zagribelnyy
- Insilico
Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, P.O.
Box 145748, Masdar City, Abu Dhabi United Arab Emirates
| | - Alex Malyshev
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Sergei Evteev
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Victor Terentiev
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Petrina Kamya
- Insilico
Medicine Canada Inc., 3710-1250 René-Lévesque Blvd W, Montreal, Quebec, Canada H3B 4W8
| | - Dmitry Bezrukov
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Alex Aliper
- Insilico
Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, P.O.
Box 145748, Masdar City, Abu Dhabi United Arab Emirates
| | - Feng Ren
- Insilico
Medicine Shanghai Ltd., Suite 901, Tower C, Changtai Plaza, 2889 Jinke Road, Pudong New District, Shanghai 201203, China
| | - Alex Zhavoronkov
- Insilico
Medicine Hong Kong Ltd., Science Park East Avenue, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| |
Collapse
|
8
|
Lawrenz M, Svensson M, Kato M, Dingley KH, Chief Elk J, Nie Z, Zou Y, Kaplan Z, Lagiakos HR, Igawa H, Therrien E. A Computational Physics-based Approach to Predict Unbound Brain-to-Plasma Partition Coefficient, K p,uu. J Chem Inf Model 2023. [PMID: 37267072 DOI: 10.1021/acs.jcim.3c00150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
The blood-brain barrier (BBB) plays a critical role in preventing harmful endogenous and exogenous substances from penetrating the brain. Optimal brain penetration of small-molecule central nervous system (CNS) drugs is characterized by a high unbound brain/plasma ratio (Kp,uu). While various medicinal chemistry strategies and in silico models have been reported to improve BBB penetration, they have limited application in predicting Kp,uu directly. We describe a physics-based computational approach, a quantum mechanics (QM)-based energy of solvation (E-sol), to predict Kp,uu. Prospective application of this method in internal CNS drug discovery programs highlights the utility and accuracy of this new method, which showed a categorical accuracy of 79% and an R2 of 0.61 from a linear regression model.
Collapse
Affiliation(s)
- Morgan Lawrenz
- Schrödinger Inc., San Diego, California 92122, United States
| | - Mats Svensson
- Schrödinger Inc., New York, New York 10036, United States
| | - Mitsunori Kato
- Schrödinger Inc., New York, New York 10036, United States
| | | | | | - Zhe Nie
- Schrödinger Inc., San Diego, California 92122, United States
| | - Yefen Zou
- Schrödinger Inc., San Diego, California 92122, United States
| | - Zachary Kaplan
- Schrödinger Inc., New York, New York 10036, United States
| | | | - Hideyuki Igawa
- Schrödinger Inc., New York, New York 10036, United States
| | - Eric Therrien
- Schrödinger Inc., New York, New York 10036, United States
| |
Collapse
|
9
|
Yu Y, Huang J, He H, Han J, Ye G, Xu T, Sun X, Chen X, Ren X, Li C, Li H, Huang W, Liu Y, Wang X, Gao Y, Cheng N, Guo N, Chen X, Feng J, Hua Y, Liu C, Zhu G, Xie Z, Yao L, Zhong W, Chen X, Liu W, Li H. Accelerated Discovery of Macrocyclic CDK2 Inhibitor QR-6401 by Generative Models and Structure-Based Drug Design. ACS Med Chem Lett 2023; 14:297-304. [PMID: 36923916 PMCID: PMC10009793 DOI: 10.1021/acsmedchemlett.2c00515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 01/19/2023] [Indexed: 02/11/2023] Open
Abstract
Selective CDK2 inhibitors have the potential to provide effective therapeutics for CDK2-dependent cancers and for combating drug resistance due to high cyclin E1 (CCNE1) expression intrinsically or CCNE1 amplification induced by treatment of CDK4/6 inhibitors. Generative models that take advantage of deep learning are being increasingly integrated into early drug discovery for hit identification and lead optimization. Here we report the discovery of a highly potent and selective macrocyclic CDK2 inhibitor QR-6401 (23) accelerated by the application of generative models and structure-based drug design (SBDD). QR-6401 (23) demonstrated robust antitumor efficacy in an OVCAR3 ovarian cancer xenograft model via oral administration.
Collapse
Affiliation(s)
- Yang Yu
- Tencent
AI Lab, Tencent, Shenzhen 518057, China
| | | | - Hu He
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Jing Han
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Geyan Ye
- Tencent
AI Lab, Tencent, Shenzhen 518057, China
| | - Tingyang Xu
- Tencent
AI Lab, Tencent, Shenzhen 518057, China
| | | | - Xiumei Chen
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Xiaoming Ren
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Chunlai Li
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Huijuan Li
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Wei Huang
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Yangyang Liu
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Xinjuan Wang
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Yongzhi Gao
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Nianhe Cheng
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Na Guo
- BioDuro-Sundia, Shanghai, 200131, China
| | - Xibo Chen
- BioDuro-Sundia, Shanghai, 200131, China
| | | | - Yuxia Hua
- BioDuro-Sundia, Beijing, 102200, China
| | - Chong Liu
- BioDuro-Sundia, Beijing, 102200, China
| | - Guoyun Zhu
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Zhi Xie
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Lili Yao
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Wenge Zhong
- Regor
Therapeutics Group, Shanghai, 201210, China
| | - Xinde Chen
- Tencent
AI Lab, Tencent, Shenzhen 518057, China
| | - Wei Liu
- Tencent
AI Lab, Tencent, Shenzhen 518057, China
| | - Hailong Li
- Regor
Therapeutics Group, Shanghai, 201210, China
| |
Collapse
|
10
|
Abstract
![]()
One application area
of computational methods in drug discovery
is the automated design of small molecules. Despite the large number
of publications describing methods and their application in both retrospective
and prospective studies, there is a lack of agreement on terminology
and key attributes to distinguish these various systems. We introduce
Automated Chemical Design (ACD) Levels to clearly define the level
of autonomy along the axes of ideation and decision making. To fully
illustrate this framework, we provide literature exemplars and place
some notable methods and applications into the levels. The ACD framework
provides a common language for describing automated small molecule
design systems and enables medicinal chemists to better understand
and evaluate such systems.
Collapse
Affiliation(s)
- Brian Goldman
- Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02139, United States
| | - Steven Kearnes
- Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02139, United States
| | - Trevor Kramer
- Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02139, United States
| | - Patrick Riley
- Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02139, United States
| | - W Patrick Walters
- Relay Therapeutics, 399 Binney Street, Cambridge, Massachusetts 02139, United States
| |
Collapse
|
11
|
Tang H, Jensen K, Houang E, McRobb FM, Bhat S, Svensson M, Bochevarov A, Day T, Dahlgren MK, Bell JA, Frye L, Skene RJ, Lewis JH, Osborne JD, Tierney JP, Gordon JA, Palomero MA, Gallati C, Chapman RSL, Jones DR, Hirst KL, Sephton M, Chauhan A, Sharpe A, Tardia P, Dechaux EA, Taylor A, Waddell RD, Valentine A, Janssens HB, Aziz O, Bloomfield DE, Ladha S, Fraser IJ, Ellard JM. Discovery of a Novel Class of d-Amino Acid Oxidase Inhibitors Using the Schrödinger Computational Platform. J Med Chem 2022; 65:6775-6802. [PMID: 35482677 DOI: 10.1021/acs.jmedchem.2c00118] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
d-Serine is a coagonist of the N-methyl d-aspartate (NMDA) receptor, a key excitatory neurotransmitter receptor. In the brain, d-serine is synthesized from its l-isomer by serine racemase and is metabolized by the D-amino acid oxidase (DAO, DAAO). Many studies have linked decreased d-serine concentration and/or increased DAO expression and enzyme activity to NMDA dysfunction and schizophrenia. Thus, it is feasible to employ DAO inhibitors for the treatment of schizophrenia and other indications. Powered by the Schrödinger computational modeling platform, we initiated a research program to identify novel DAO inhibitors with the best-in-class properties. The program execution leveraged an hDAO FEP+ model to prospectively predict compound potency. A new class of DAO inhibitors with desirable properties has been discovered from this endeavor. Our modeling technology on this program has not only enhanced the efficiency of structure-activity relationship development but also helped to identify a previously unexplored subpocket for further optimization.
Collapse
Affiliation(s)
- Haifeng Tang
- Schrödinger Inc., New York, New York 10036, United States
| | | | - Evelyne Houang
- Schrödinger Inc., New York, New York 10036, United States
| | - Fiona M McRobb
- Schrödinger Inc., New York, New York 10036, United States
| | - Sathesh Bhat
- Schrödinger Inc., New York, New York 10036, United States
| | - Mats Svensson
- Schrödinger Inc., New York, New York 10036, United States
| | - Art Bochevarov
- Schrödinger Inc., New York, New York 10036, United States
| | - Tyler Day
- Schrödinger Inc., New York, New York 10036, United States
| | | | - Jeffery A Bell
- Schrödinger Inc., New York, New York 10036, United States
| | - Leah Frye
- Schrödinger Inc., New York, New York 10036, United States
| | - Robert J Skene
- Takeda Development Center Americas, Inc., San Diego, California 92121, United States
| | - James H Lewis
- Charles River Laboratories, Saffron Walden, Essex CB10 1XL, U.K
| | - James D Osborne
- Charles River Laboratories, Saffron Walden, Essex CB10 1XL, U.K
| | - Jason P Tierney
- Charles River Laboratories, Saffron Walden, Essex CB10 1XL, U.K
| | - James A Gordon
- Charles River Laboratories, Saffron Walden, Essex CB10 1XL, U.K
| | | | | | | | - Daniel R Jones
- Charles River Laboratories, Saffron Walden, Essex CB10 1XL, U.K
| | - Kim L Hirst
- Charles River Laboratories, Saffron Walden, Essex CB10 1XL, U.K
| | - Mark Sephton
- Charles River Laboratories, Saffron Walden, Essex CB10 1XL, U.K
| | - Alka Chauhan
- Charles River Laboratories, Saffron Walden, Essex CB10 1XL, U.K
| | - Andrew Sharpe
- Charles River Laboratories, Saffron Walden, Essex CB10 1XL, U.K
| | - Piero Tardia
- Charles River Laboratories, Saffron Walden, Essex CB10 1XL, U.K
| | | | - Andrea Taylor
- Charles River Laboratories, Harlow, Essex CM19 5TR, U.K
| | | | | | - Holden B Janssens
- Charles River Laboratories, South San Francisco, California 94080, United States
| | - Omar Aziz
- Charles River Laboratories, Harlow, Essex CM19 5TR, U.K
| | | | - Sandeep Ladha
- Charles River Laboratories, Saffron Walden, Essex CB10 1XL, U.K
| | - Ian J Fraser
- Charles River Laboratories, Saffron Walden, Essex CB10 1XL, U.K
| | - John M Ellard
- Charles River Laboratories, Saffron Walden, Essex CB10 1XL, U.K.,Charles River Laboratories, Harlow, Essex CM19 5TR, U.K
| |
Collapse
|