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Brodney MD, Bakken G, Butler CR, Klug-McLeod J, Owen R, Sng ST. Integrated design environment: A multi-use platform for design idea capture, evaluation, and tracking in medicinal chemistry. J Comput Chem 2023; 44:788-800. [PMID: 36471909 DOI: 10.1002/jcc.27041] [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: 05/13/2022] [Revised: 09/07/2022] [Accepted: 10/24/2022] [Indexed: 12/12/2022]
Abstract
An integrated design environment (IDE) has been developed that allows the capture of design ideas, virtual compounds, and design hypotheses for medicinal chemistry projects. Specific consideration for rational molecular design, including design strategy and tactics, as well as comparator reference compounds have been incorporated to more easily convey the proposed design idea. A hierarchical tree architecture and customizable layouts allow for facile browsing across multiple programs and rapid examination of both ongoing and newly designed virtual compounds enabling centralized team discussions to ensure the most efficient prosecution of a queue of these target compounds. Additionally, a "whiteboard" module was incorporated for the rapid evaluation of virtual compounds against a suite of computational models enabling real-time design and triage. Finally, aggregation of cross-project design data enables broader analyses that can indicate portfolio-wide design challenges.
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Affiliation(s)
- Marian D Brodney
- Molecular Informatics, Pfizer Global, Cambridge, Massachusetts, USA
| | - Gregory Bakken
- Research and Development, Cadence Design Systems Inc, San Jose, California, USA
| | - Christopher R Butler
- Medicinal Chemistry, Vertex Pharmaceuticals Incorporated, Boston, Massachusetts, USA
| | | | | | - Shao-Tien Sng
- Pfizer Global Research and Development, Groton, Connecticut, USA
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2
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Cox PB, Gupta R. Contemporary Computational Applications and Tools in Drug Discovery. ACS Med Chem Lett 2022; 13:1016-1029. [DOI: 10.1021/acsmedchemlett.1c00662] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Affiliation(s)
- Philip B. Cox
- Drug Discovery Science and Technology, AbbVie, 1 North Waukegan Road, North Chicago, Illinois 60064-6217, United States
| | - Rishi Gupta
- Drug Discovery Science and Technology, AbbVie, 1 North Waukegan Road, North Chicago, Illinois 60064-6217, United States
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3
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Zhao L, Ciallella HL, Aleksunes LM, Zhu H. Advancing computer-aided drug discovery (CADD) by big data and data-driven machine learning modeling. Drug Discov Today 2020; 25:1624-1638. [PMID: 32663517 PMCID: PMC7572559 DOI: 10.1016/j.drudis.2020.07.005] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 06/26/2020] [Accepted: 07/06/2020] [Indexed: 02/06/2023]
Abstract
Advancing a new drug to market requires substantial investments in time as well as financial resources. Crucial bioactivities for drug candidates, including their efficacy, pharmacokinetics (PK), and adverse effects, need to be investigated during drug development. With advancements in chemical synthesis and biological screening technologies over the past decade, a large amount of biological data points for millions of small molecules have been generated and are stored in various databases. These accumulated data, combined with new machine learning (ML) approaches, such as deep learning, have shown great potential to provide insights into relevant chemical structures to predict in vitro, in vivo, and clinical outcomes, thereby advancing drug discovery and development in the big data era.
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Affiliation(s)
- Linlin Zhao
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA
| | - Heather L Ciallella
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA
| | - Lauren M Aleksunes
- Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ 08854, USA
| | - Hao Zhu
- The Rutgers Center for Computational and Integrative Biology, Camden, NJ 08102, USA; Department of Chemistry, Rutgers University, Camden, NJ 08102, USA.
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4
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Cruz-Monteagudo M, Schürer S, Tejera E, Pérez-Castillo Y, Medina-Franco JL, Sánchez-Rodríguez A, Borges F. Systemic QSAR and phenotypic virtual screening: chasing butterflies in drug discovery. Drug Discov Today 2017; 22:994-1007. [PMID: 28274840 PMCID: PMC5487293 DOI: 10.1016/j.drudis.2017.02.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2016] [Revised: 02/02/2017] [Accepted: 02/27/2017] [Indexed: 12/20/2022]
Abstract
Current advances in systems biology suggest a new change of paradigm reinforcing the holistic nature of the drug discovery process. According to the principles of systems biology, a simple drug perturbing a network of targets can trigger complex reactions. Therefore, it is possible to connect initial events with final outcomes and consequently prioritize those events, leading to a desired effect. Here, we introduce a new concept, 'Systemic Chemogenomics/Quantitative Structure-Activity Relationship (QSAR)'. To elaborate on the concept, relevant information surrounding it is addressed. The concept is challenged by implementing a systemic QSAR approach for phenotypic virtual screening (VS) of candidate ligands acting as neuroprotective agents in Parkinson's disease (PD). The results support the suitability of the approach for the phenotypic prioritization of drug candidates.
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Affiliation(s)
- Maykel Cruz-Monteagudo
- CIQUP/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal.
| | - Stephan Schürer
- Department of Pharmacology, Miller School of Medicine and Center for Computational Science, University of Miami, Miami, FL 33136, USA
| | - Eduardo Tejera
- Instituto de Investigaciones Biomédicas (IIB), Universidad de Las Américas, 170513 Quito, Ecuador
| | - Yunierkis Pérez-Castillo
- Sección Físico Química y Matemáticas, Departamento de Química, Universidad Técnica Particular de Loja, San Cayetano Alto S/N, EC1101608 Loja, Ecuador
| | - José L Medina-Franco
- Universidad Nacional Autónoma de México, Departamento de Farmacia, Facultad de Química, Avenida Universidad 3000, Mexico City, 04510, Mexico
| | - Aminael Sánchez-Rodríguez
- Departamento de Ciencias Naturales, Universidad Técnica Particular de Loja, Calle París S/N, EC1101608 Loja, Ecuador
| | - Fernanda Borges
- CIQUP/Departamento de Química e Bioquímica, Faculdade de Ciências, Universidade do Porto, Porto 4169-007, Portugal.
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5
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Moretti L, Sartori L. Software Infrastructure for Computer-aided Drug Discovery and Development, a Practical Example with Guidelines. Mol Inform 2016; 35:382-90. [DOI: 10.1002/minf.201501037] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2015] [Accepted: 05/19/2016] [Indexed: 11/08/2022]
Affiliation(s)
- Loris Moretti
- Drug Discovery Program, Department of Experimental Oncology; European Institute of Oncology; Via Adamello 16 20139 Milan Italy
- Nuevolution A/S; Rønnegade 8 DK-2100 Copenhagen Denmark
| | - Luca Sartori
- Drug Discovery Program, Department of Experimental Oncology; European Institute of Oncology; Via Adamello 16 20139 Milan Italy
- Experimental Therapeutics Unit; IFOM - The FIRC Institute of Molecular Oncology; Via Adamello 16 20139 Milan Italy
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6
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7
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Finding the right approach to big data-driven medicinal chemistry. Future Med Chem 2015; 7:1213-6. [DOI: 10.4155/fmc.15.58] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
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8
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Multi-objective optimization methods in drug design. DRUG DISCOVERY TODAY. TECHNOLOGIES 2014; 10:e427-35. [PMID: 24050140 DOI: 10.1016/j.ddtec.2013.02.001] [Citation(s) in RCA: 91] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Drug discovery is a challenging multi-objective problem where numerous pharmaceutically important objectives need to be adequately satisfied for a solution to be found. The problem is characterized by vast, complex solution spaces further perplexed by the presence of conflicting objectives. Multi-objective optimization methods, designed specifically to address such problems, have been introduced to the drug discovery field over a decade ago and have steadily gained in acceptance ever since. This paper reviews the latest multi-objective methods and applications reported in the literature, specifically in quantitative structure–activity modeling, docking, de novo design and library design. Further, the paper reports on related developments in drug discovery research and advances in the multi-objective optimization field.
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Lusher SJ, McGuire R, van Schaik RC, Nicholson CD, de Vlieg J. Data-driven medicinal chemistry in the era of big data. Drug Discov Today 2013; 19:859-68. [PMID: 24361338 DOI: 10.1016/j.drudis.2013.12.004] [Citation(s) in RCA: 70] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2013] [Revised: 11/11/2013] [Accepted: 12/11/2013] [Indexed: 10/25/2022]
Abstract
Science, and the way we undertake research, is changing. The increasing rate of data generation across all scientific disciplines is providing incredible opportunities for data-driven research, with the potential to transform our current practices. The exploitation of so-called 'big data' will enable us to undertake research projects never previously possible but should also stimulate a re-evaluation of all our data practices. Data-driven medicinal chemistry approaches have the potential to improve decision making in drug discovery projects, providing that all researchers embrace the role of 'data scientist' and uncover the meaningful relationships and patterns in available data.
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Affiliation(s)
- Scott J Lusher
- Netherlands eScience Center, Amsterdam, The Netherlands; Computational Drug Discovery Group, Radboud University, Nijmegen, The Netherlands.
| | - Ross McGuire
- Computational Drug Discovery Group, Radboud University, Nijmegen, The Netherlands; Bioaxis Research, Pivot Park, Oss, The Netherlands
| | | | | | - Jacob de Vlieg
- Netherlands eScience Center, Amsterdam, The Netherlands; Computational Drug Discovery Group, Radboud University, Nijmegen, The Netherlands
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10
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Ginman T, Viklund J, Malmström J, Blid J, Emond R, Forsblom R, Johansson A, Kers A, Lake F, Sehgelmeble F, Sterky KJ, Bergh M, Lindgren A, Johansson P, Jeppsson F, Fälting J, Gravenfors Y, Rahm F. Core refinement toward permeable β-secretase (BACE-1) inhibitors with low hERG activity. J Med Chem 2013; 56:4181-205. [PMID: 23126626 DOI: 10.1021/jm3011349] [Citation(s) in RCA: 36] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
By use of iterative design aided by predictive models for target affinity, brain permeability, and hERG activity, novel and diverse compounds based on cyclic amidine and guanidine cores were synthesized with the goal of finding BACE-1 inhibitors as a treatment for Alzheimer's disease. Since synthesis feasibility had low priority in the design of the cores, an extensive synthesis effort was needed to make the relevant compounds. Syntheses of these compounds are reported, together with physicochemical properties and structure-activity relationships based on in vitro data. Four crystal structures of diverse amidines binding in the active site are deposited and discussed. Inhibitors of BACE-1 with 3 μM to 32 nM potencies in cells are shown, together with data on in vivo brain exposure levels for four compounds. The results presented show the importance of the core structure for the profile of the final compounds.
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Affiliation(s)
- Tobias Ginman
- Department of Medicinal Chemistry, AstraZeneca R&D Södertälje, SE-151 85, Södertälje, Sweden
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11
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Analyzing compound and project progress through multi-objective-based compound quality assessment. Future Med Chem 2013; 5:753-67. [DOI: 10.4155/fmc.13.45] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background: Compound-quality scoring methods designed to evaluate multiple drug properties concurrently are useful to analyze and prioritize output from drug-design efforts. However, formalized multiparameter optimization approaches are not widely used in drug design. Methods: We rank molecules synthesized in drug-discovery projects using simple and aggregated desirability functions reflecting medicinal chemistry ‘rules’. Our quality score deals transparently with missing data, a key requirement in drug-hunting projects where data availability is often limited. We further estimate confidence in the interpretation of such a compound-quality measure. Conclusion: Scores and associated confidences provide systematic insight in the quality of emerging chemical equity. Tracking quality of synthetic output over time yields valuable insight into the progress of drug-design teams, with potential applications in risk and resource management of a drug portfolio.
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12
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Bonnet P. Is chemical synthetic accessibility computationally predictable for drug and lead-like molecules? A comparative assessment between medicinal and computational chemists. Eur J Med Chem 2012; 54:679-89. [DOI: 10.1016/j.ejmech.2012.06.024] [Citation(s) in RCA: 33] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2012] [Revised: 06/04/2012] [Accepted: 06/12/2012] [Indexed: 11/27/2022]
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13
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Baede EJ, den Bekker E, Boiten JW, Cronin D, van Gammeren R, de Vlieg J. Integrated Project Views: Decision Support Platform for Drug Discovery Project Teams. J Chem Inf Model 2012; 52:1438-49. [DOI: 10.1021/ci200253g] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Eric J. Baede
- Discovery Informatics, Molecular
Design and Informatics Department, MSD, Molenstraat 110, 5342 CC Oss,
The Netherlands
| | - Ernest den Bekker
- Discovery Informatics, Molecular
Design and Informatics Department, MSD, Molenstraat 110, 5342 CC Oss,
The Netherlands
| | - Jan-Willem Boiten
- Discovery Informatics, Molecular
Design and Informatics Department, MSD, Molenstraat 110, 5342 CC Oss,
The Netherlands
| | - Deborah Cronin
- Discovery Informatics, Molecular
Design and Informatics Department, MSD, Molenstraat 110, 5342 CC Oss,
The Netherlands
| | - Rob van Gammeren
- Discovery Informatics, Molecular
Design and Informatics Department, MSD, Molenstraat 110, 5342 CC Oss,
The Netherlands
| | - Jacob de Vlieg
- Discovery Informatics, Molecular
Design and Informatics Department, MSD, Molenstraat 110, 5342 CC Oss,
The Netherlands
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14
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Ritchie TJ, McLay IM. Should medicinal chemists do molecular modelling? Drug Discov Today 2012; 17:534-7. [DOI: 10.1016/j.drudis.2012.01.005] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2011] [Revised: 12/01/2011] [Accepted: 01/09/2012] [Indexed: 11/27/2022]
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15
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Discovery of small molecule cancer drugs: successes, challenges and opportunities. Mol Oncol 2012; 6:155-76. [PMID: 22440008 PMCID: PMC3476506 DOI: 10.1016/j.molonc.2012.02.004] [Citation(s) in RCA: 374] [Impact Index Per Article: 31.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2012] [Accepted: 02/20/2012] [Indexed: 02/07/2023] Open
Abstract
The discovery and development of small molecule cancer drugs has been revolutionised over the last decade. Most notably, we have moved from a one-size-fits-all approach that emphasized cytotoxic chemotherapy to a personalised medicine strategy that focuses on the discovery and development of molecularly targeted drugs that exploit the particular genetic addictions, dependencies and vulnerabilities of cancer cells. These exploitable characteristics are increasingly being revealed by our expanding understanding of the abnormal biology and genetics of cancer cells, accelerated by cancer genome sequencing and other high-throughput genome-wide campaigns, including functional screens using RNA interference. In this review we provide an overview of contemporary approaches to the discovery of small molecule cancer drugs, highlighting successes, current challenges and future opportunities. We focus in particular on four key steps: Target validation and selection; chemical hit and lead generation; lead optimization to identify a clinical drug candidate; and finally hypothesis-driven, biomarker-led clinical trials. Although all of these steps are critical, we view target validation and selection and the conduct of biology-directed clinical trials as especially important areas upon which to focus to speed progress from gene to drug and to reduce the unacceptably high attrition rate during clinical development. Other challenges include expanding the envelope of druggability for less tractable targets, understanding and overcoming drug resistance, and designing intelligent and effective drug combinations. We discuss not only scientific and technical challenges, but also the assessment and mitigation of risks as well as organizational, cultural and funding problems for cancer drug discovery and development, together with solutions to overcome the 'Valley of Death' between basic research and approved medicines. We envisage a future in which addressing these challenges will enhance our rapid progress towards truly personalised medicine for cancer patients.
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