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Seal S, Spjuth O, Hosseini-Gerami L, García-Ortegón M, Singh S, Bender A, Carpenter AE. Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank. J Chem Inf Model 2024; 64:1172-1186. [PMID: 38300851 PMCID: PMC10900289 DOI: 10.1021/acs.jcim.3c01834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/11/2024] [Accepted: 01/16/2024] [Indexed: 02/03/2024]
Abstract
Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10-14% of postmarket withdrawals. In this study, we explored the capabilities of chemical and biological data to predict cardiotoxicity, using the recently released DICTrank data set from the United States FDA. We found that such data, including protein targets, especially those related to ion channels (e.g., hERG), physicochemical properties (e.g., electrotopological state), and peak concentration in plasma offer strong predictive ability for DICT. Compounds annotated with mechanisms of action such as cyclooxygenase inhibition could distinguish between most-concern and no-concern DICT. Cell Painting features for ER stress discerned most-concern cardiotoxic from nontoxic compounds. Models based on physicochemical properties provided substantial predictive accuracy (AUCPR = 0.93). With the availability of omics data in the future, using biological data promises enhanced predictability and deeper mechanistic insights, paving the way for safer drug development. All models from this study are available at https://broad.io/DICTrank_Predictor.
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Affiliation(s)
- Srijit Seal
- Imaging
Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - Ola Spjuth
- Department
of Pharmaceutical Biosciences and Science for Life Laboratory, Uppsala University, Box
591, SE-75124 Uppsala, Sweden
| | - Layla Hosseini-Gerami
- Ignota
Labs, The Bradfield Centre, Cambridge Science Park, County Hall, Westminster Bridge Road, Cambridge CB4 0GA, U.K.
| | - Miguel García-Ortegón
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - Shantanu Singh
- Imaging
Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
| | - Andreas Bender
- Yusuf
Hamied Department of Chemistry, University
of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K.
| | - Anne E. Carpenter
- Imaging
Platform, Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, United States
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Seal S, Spjuth O, Hosseini-Gerami L, García-Ortegón M, Singh S, Bender A, Carpenter AE. Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA DICTrank. bioRxiv 2023:2023.10.15.562398. [PMID: 37905146 PMCID: PMC10614794 DOI: 10.1101/2023.10.15.562398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Drug-induced cardiotoxicity (DICT) is a major concern in drug development, accounting for 10-14% of postmarket withdrawals. In this study, we explored the capabilities of various chemical and biological data to predict cardiotoxicity, using the recently released Drug-Induced Cardiotoxicity Rank (DICTrank) dataset from the United States FDA. We analyzed a diverse set of data sources, including physicochemical properties, annotated mechanisms of action (MOA), Cell Painting, Gene Expression, and more, to identify indications of cardiotoxicity. We found that such data, including protein targets, especially those related to ion channels (such as hERG), physicochemical properties (such as electrotopological state) as well as peak concentration in plasma offer strong predictive ability as well as valuable insights into DICT. We also found compounds annotated with particular mechanisms of action, such as cyclooxygenase inhibition, could distinguish between most-concern and no-concern DICT compounds. Cell Painting features related to ER stress discern the most-concern cardiotoxic compounds from non-toxic compounds. While models based on physicochemical properties currently provide substantial predictive accuracy (AUCPR = 0.93), this study also underscores the potential benefits of incorporating more comprehensive biological data in future DICT predictive models. With the availability of - omics data in the future, using biological data promises enhanced predictability and delivers deeper mechanistic insights, paving the way for safer therapeutic drug development. All models and data used in this study are publicly released at https://broad.io/DICTrank_Predictor.
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Affiliation(s)
- Srijit Seal
- Imaging Platform, Broad Institute of MIT and Harvard, US
| | - Ola Spjuth
- Department of Pharmaceutical Biosciences, Uppsala University, Sweden
| | | | | | - Shantanu Singh
- Imaging Platform, Broad Institute of MIT and Harvard, US
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Hosseini-Gerami L, Hernansaiz Ballesteros R, Liu A, Broughton H, Collier DA, Bender A. MAVEN: compound mechanism of action analysis and visualisation using transcriptomics and compound structure data in R/Shiny. BMC Bioinformatics 2023; 24:344. [PMID: 37715141 PMCID: PMC10502988 DOI: 10.1186/s12859-023-05416-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Accepted: 07/18/2023] [Indexed: 09/17/2023] Open
Abstract
BACKGROUND Understanding the Mechanism of Action (MoA) of a compound is an often challenging but equally crucial aspect of drug discovery that can help improve both its efficacy and safety. Computational methods to aid MoA elucidation usually either aim to predict direct drug targets, or attempt to understand modulated downstream pathways or signalling proteins. Such methods usually require extensive coding experience and results are often optimised for further computational processing, making them difficult for wet-lab scientists to perform, interpret and draw hypotheses from. RESULTS To address this issue, we in this work present MAVEN (Mechanism of Action Visualisation and Enrichment), an R/Shiny app which allows for GUI-based prediction of drug targets based on chemical structure, combined with causal reasoning based on causal protein-protein interactions and transcriptomic perturbation signatures. The app computes a systems-level view of the mechanism of action of the input compound. This is visualised as a sub-network linking predicted or known targets to modulated transcription factors via inferred signalling proteins. The tool includes a selection of MSigDB gene set collections to perform pathway enrichment on the resulting network, and also allows for custom gene sets to be uploaded by the researcher. MAVEN is hence a user-friendly, flexible tool for researchers without extensive bioinformatics or cheminformatics knowledge to generate interpretable hypotheses of compound Mechanism of Action. CONCLUSIONS MAVEN is available as a fully open-source tool at https://github.com/laylagerami/MAVEN with options to install in a Docker or Singularity container. Full documentation, including a tutorial on example data, is available at https://laylagerami.github.io/MAVEN .
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Affiliation(s)
- Layla Hosseini-Gerami
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.
- Ignota Labs, London, UK.
| | - Rosa Hernansaiz Ballesteros
- Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Bioquant, Heidelberg University, Heidelberg, Germany
| | - Anika Liu
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK
| | - Howard Broughton
- Eli Lilly and Company Centre de Investigacion, Alcobendas, Spain
| | - David Andrew Collier
- Eli Lilly and Company, Bracknell, UK
- King's College London, and Genetics and Genomics Consulting, Surrey, UK
| | - Andreas Bender
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.
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Hosseini-Gerami L, Higgins IA, Collier DA, Laing E, Evans D, Broughton H, Bender A. Benchmarking causal reasoning algorithms for gene expression-based compound mechanism of action analysis. BMC Bioinformatics 2023; 24:154. [PMID: 37072707 PMCID: PMC10111792 DOI: 10.1186/s12859-023-05277-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Accepted: 04/06/2023] [Indexed: 04/20/2023] Open
Abstract
BACKGROUND Elucidating compound mechanism of action (MoA) is beneficial to drug discovery, but in practice often represents a significant challenge. Causal Reasoning approaches aim to address this situation by inferring dysregulated signalling proteins using transcriptomics data and biological networks; however, a comprehensive benchmarking of such approaches has not yet been reported. Here we benchmarked four causal reasoning algorithms (SigNet, CausalR, CausalR ScanR and CARNIVAL) with four networks (the smaller Omnipath network vs. 3 larger MetaBase™ networks), using LINCS L1000 and CMap microarray data, and assessed to what extent each factor dictated the successful recovery of direct targets and compound-associated signalling pathways in a benchmark dataset comprising 269 compounds. We additionally examined impact on performance in terms of the functions and roles of protein targets and their connectivity bias in the prior knowledge networks. RESULTS According to statistical analysis (negative binomial model), the combination of algorithm and network most significantly dictated the performance of causal reasoning algorithms, with the SigNet recovering the greatest number of direct targets. With respect to the recovery of signalling pathways, CARNIVAL with the Omnipath network was able to recover the most informative pathways containing compound targets, based on the Reactome pathway hierarchy. Additionally, CARNIVAL, SigNet and CausalR ScanR all outperformed baseline gene expression pathway enrichment results. We found no significant difference in performance between L1000 data or microarray data, even when limited to just 978 'landmark' genes. Notably, all causal reasoning algorithms also outperformed pathway recovery based on input DEGs, despite these often being used for pathway enrichment. Causal reasoning methods performance was somewhat correlated with connectivity and biological role of the targets. CONCLUSIONS Overall, we conclude that causal reasoning performs well at recovering signalling proteins related to compound MoA upstream from gene expression changes by leveraging prior knowledge networks, and that the choice of network and algorithm has a profound impact on the performance of causal reasoning algorithms. Based on the analyses presented here this is true for both microarray-based gene expression data as well as those based on the L1000 platform.
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Affiliation(s)
- Layla Hosseini-Gerami
- Department of Chemistry, Centre for Molecular Informatics, Cambridge, UK
- Ignota Labs, London, UK
| | | | - David A Collier
- Eli Lilly and Company, Bracknell, UK
- Social, Genetic and Developmental Psychiatry Centre, IoPPN, Kings's College London, London, UK
- Genetic and Genomic Consulting Ltd, Farnham, UK
| | - Emma Laing
- Eli Lilly and Company, Bracknell, UK
- GSK, Stevenage, UK
| | - David Evans
- Eli Lilly and Company, Bracknell, UK
- DeepMind, London, UK
| | - Howard Broughton
- Centre de Investigación, Eli Lilly and Company, Alcobendas, Spain
| | - Andreas Bender
- Department of Chemistry, Centre for Molecular Informatics, Cambridge, UK.
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Hosseini-Gerami L, Ficulle E, Humphryes-Kirilov N, Airey DC, Scherschel J, Kananathan S, Eastwood BJ, Bose S, Collier DA, Laing E, Evans D, Broughton H, Bender A. Mechanism of action deconvolution of the small-molecule pathological tau aggregation inhibitor Anle138b. Alzheimers Res Ther 2023; 15:52. [PMID: 36918909 PMCID: PMC10012450 DOI: 10.1186/s13195-023-01182-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 02/06/2023] [Indexed: 03/16/2023]
Abstract
BACKGROUND A key histopathological hallmark of Alzheimer's disease (AD) is the presence of neurofibrillary tangles of aggregated microtubule-associated protein tau in neurons. Anle138b is a small molecule which has previously shown efficacy in mice in reducing tau aggregates and rescuing AD disease phenotypes. METHODS In this work, we employed bioinformatics analysis-including pathway enrichment and causal reasoning-of an in vitro tauopathy model. The model consisted of cultured rat cortical neurons either unseeded or seeded with tau aggregates derived from human AD patients, both of which were treated with Anle138b to generate hypotheses for its mode of action. In parallel, we used a collection of human target prediction models to predict direct targets of Anle138b based on its chemical structure. RESULTS Combining the different approaches, we found evidence supporting the hypothesis that the action of Anle138b involves several processes which are key to AD progression, including cholesterol homeostasis and neuroinflammation. On the pathway level, we found significantly enriched pathways related to these two processes including those entitled "Superpathway of cholesterol biosynthesis" and "Granulocyte adhesion and diapedesis". With causal reasoning, we inferred differential activity of SREBF1/2 (involved in cholesterol regulation) and mediators of the inflammatory response such as NFKB1 and RELA. Notably, our findings were also observed in Anle138b-treated unseeded neurons, meaning that the inferred processes are independent of tau pathology and thus represent the direct action of the compound in the cellular system. Through structure-based ligand-target prediction, we predicted the intracellular cholesterol carrier NPC1 as well as NF-κB subunits as potential targets of Anle138b, with structurally similar compounds in the model training set known to target the same proteins. CONCLUSIONS This study has generated feasible hypotheses for the potential mechanism of action of Anle138b, which will enable the development of future molecular interventions aiming to reduce tau pathology in AD patients.
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Affiliation(s)
- Layla Hosseini-Gerami
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.,AbsoluteAi Ltd, London, UK
| | - Elena Ficulle
- Eli Lilly and Company, Windlesham, UK.,Zifo RnD Solutions, London, UK
| | | | - David C Airey
- Eli Lilly and Company, Corporate Centre, Indianapolis, IN, USA
| | | | | | - Brian J Eastwood
- Eli Lilly and Company, Windlesham, UK.,Eli Lilly and Company, Bracknell, UK.,Eli Lilly and Company (Retired), Bracknell, UK
| | - Suchira Bose
- Eli Lilly and Company, Windlesham, UK.,Eli Lilly and Company, Bracknell, UK
| | - David A Collier
- Eli Lilly and Company, Windlesham, UK.,Eli Lilly and Company, Bracknell, UK.,Social, Genetic and Developmental Psychiatry Centre, IoPPN, Kings's College London and Genetic and Genomic Consulting Ltd, Farnham, UK
| | - Emma Laing
- Eli Lilly and Company, Windlesham, UK.,GSK, Stevenage, UK
| | - David Evans
- Eli Lilly and Company, Windlesham, UK.,DeepMind, London, UK
| | | | - Andreas Bender
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, UK.
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Zhu Y, Yang H, Han L, Mervin LH, Hosseini-Gerami L, Li P, Wright P, Trapotsi MA, Liu K, Fan TP, Bender A. In silico prediction and biological assessment of novel angiogenesis modulators from traditional Chinese medicine. Front Pharmacol 2023; 14:1116081. [PMID: 36817116 PMCID: PMC9937659 DOI: 10.3389/fphar.2023.1116081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 01/20/2023] [Indexed: 02/05/2023] Open
Abstract
Uncontrolled angiogenesis is a common denominator underlying many deadly and debilitating diseases such as myocardial infarction, chronic wounds, cancer, and age-related macular degeneration. As the current range of FDA-approved angiogenesis-based medicines are far from meeting clinical demands, the vast reserve of natural products from traditional Chinese medicine (TCM) offers an alternative source for developing pro-angiogenic or anti-angiogenic modulators. Here, we investigated 100 traditional Chinese medicine-derived individual metabolites which had reported gene expression in MCF7 cell lines in the Gene Expression Omnibus (GSE85871). We extracted literature angiogenic activities for 51 individual metabolites, and subsequently analysed their predicted targets and differentially expressed genes to understand their mechanisms of action. The angiogenesis phenotype was used to generate decision trees for rationalising the poly-pharmacology of known angiogenesis modulators such as ferulic acid and curculigoside and validated by an in vitro endothelial tube formation assay and a zebrafish model of angiogenesis. Moreover, using an in silico model we prospectively examined the angiogenesis-modulating activities of the remaining 49 individual metabolites. In vitro, tetrahydropalmatine and 1 beta-hydroxyalantolactone stimulated, while cinobufotalin and isoalantolactone inhibited endothelial tube formation. In vivo, ginsenosides Rb3 and Rc, 1 beta-hydroxyalantolactone and surprisingly cinobufotalin, restored angiogenesis against PTK787-induced impairment in zebrafish. In the absence of PTK787, deoxycholic acid and ursodeoxycholic acid did not affect angiogenesis. Despite some limitations, these results suggest further refinements of in silico prediction combined with biological assessment will be a valuable platform for accelerating the research and development of natural products from traditional Chinese medicine and understanding their mechanisms of action, and also for other traditional medicines for the prevention and treatment of angiogenic diseases.
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Affiliation(s)
- Yingli Zhu
- Department of Clinical Chinese Pharmacy, School of Chinese Material Medica, Beijing University of Chinese Medicine, Beijing, China,Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom,Department of Pharmacology, University of Cambridge, Cambridge, United Kingdom
| | - Hongbin Yang
- Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom
| | - Liwen Han
- Engineering Research Center of Zebrafish Models for Human Diseases and Drug Screening of Shandong Province, Biology Institute, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China,School of Pharmacy and Pharmaceutical Science, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, China
| | - Lewis H. Mervin
- Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom
| | - Layla Hosseini-Gerami
- Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom
| | - Peihai Li
- Engineering Research Center of Zebrafish Models for Human Diseases and Drug Screening of Shandong Province, Biology Institute, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China
| | - Peter Wright
- Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom
| | - Maria-Anna Trapotsi
- Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom
| | - Kechun Liu
- Engineering Research Center of Zebrafish Models for Human Diseases and Drug Screening of Shandong Province, Biology Institute, Qilu University of Technology, Shandong Academy of Sciences, Jinan, China
| | - Tai-Ping Fan
- Department of Pharmacology, University of Cambridge, Cambridge, United Kingdom,*Correspondence: Tai-Ping Fan, ; Andreas Bender,
| | - Andreas Bender
- Department of Chemistry, Center for Molecular Science Informatics, University of Cambridge, Cambridge, United Kingdom,*Correspondence: Tai-Ping Fan, ; Andreas Bender,
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Trapotsi MA, Hosseini-Gerami L, Bender A. Computational analyses of mechanism of action (MoA): data, methods and integration. RSC Chem Biol 2022; 3:170-200. [PMID: 35360890 PMCID: PMC8827085 DOI: 10.1039/d1cb00069a] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 12/09/2021] [Indexed: 12/15/2022] Open
Abstract
The elucidation of a compound's Mechanism of Action (MoA) is a challenging task in the drug discovery process, but it is important in order to rationalise phenotypic findings and to anticipate potential side-effects. Bioinformatic approaches, advances in machine learning techniques and the increasing deposition of high-throughput data in public databases have significantly contributed to recent advances in the field, but it is not straightforward to decide which data and methods are most suitable to use in a given case. In this review, we focus on these methods and data and their applications in generating MoA hypotheses for subsequent experimental validation. We discuss compound-specific data such as -omics, cell morphology and bioactivity data, as well as commonly used supplementary prior knowledge such as network and pathway data, and provide information on databases where this data can be accessed. In terms of methodologies, we discuss both well-established methods (connectivity mapping, pathway enrichment) as well as more developing methods (neural networks and multi-omics integration). Finally, we review case studies where the MoA of a compound was successfully suggested from computational analysis by incorporating multiple data modalities and/or methodologies. Our aim for this review is to provide researchers with insights into the benefits and drawbacks of both the data and methods in terms of level of understanding, biases and interpretation - and to highlight future avenues of investigation which we foresee will improve the field of MoA elucidation, including greater public access to -omics data and methodologies which are capable of data integration.
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Affiliation(s)
- Maria-Anna Trapotsi
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
| | - Layla Hosseini-Gerami
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
| | - Andreas Bender
- Centre for Molecular Informatics, Yusuf Hamied Department of Chemistry, University of Cambridge UK
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Hunt P, Hosseini-Gerami L, Chrien T, Plante J, Ponting DJ, Segall M. Predicting p Ka Using a Combination of Semi-Empirical Quantum Mechanics and Radial Basis Function Methods. J Chem Inf Model 2020; 60:2989-2997. [PMID: 32357002 DOI: 10.1021/acs.jcim.0c00105] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
The acid dissociation constant (pKa) has an important influence on molecular properties crucial to compound development in synthesis, formulation, and optimization of absorption, distribution, metabolism, and excretion properties. We will present a method that combines quantum mechanical calculations, at a semi-empirical level of theory, with machine learning to accurately predict pKa for a diverse range of mono- and polyprotic compounds. The resulting model has been tested on two external data sets, one specifically used to test pKa prediction methods (SAMPL6) and the second covering known drugs containing basic functionalities. Both sets were predicted with excellent accuracy (root-mean-square errors of 0.7-1.0 log units), comparable to other methodologies using a much higher level of theory and computational cost.
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Affiliation(s)
- Peter Hunt
- Optibrium Ltd., F5-6 Blenheim House, Cambridge Innovation Park, Denny End Road, Cambridge CB25 9PB, U.K
| | - Layla Hosseini-Gerami
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, U.K
| | - Tomas Chrien
- Optibrium Ltd., F5-6 Blenheim House, Cambridge Innovation Park, Denny End Road, Cambridge CB25 9PB, U.K
| | - Jeffrey Plante
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds LS11 5PS, U.K
| | - David J Ponting
- Lhasa Limited, Granary Wharf House, 2 Canal Wharf, Leeds LS11 5PS, U.K
| | - Matthew Segall
- Optibrium Ltd., F5-6 Blenheim House, Cambridge Innovation Park, Denny End Road, Cambridge CB25 9PB, U.K
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