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Ghiandoni GM, Flanagan SR, Bodkin MJ, Nizi MG, Galera-Prat A, Brai A, Chen B, Wallace JEA, Hristozov D, Webster J, Manfroni G, Lehtiö L, Tabarrini O, Gillet VJ. Synthetically accessible de novo design using reaction vectors: Application to PARP1 inhibitors. Mol Inform 2024; 43:e202300183. [PMID: 38258328 DOI: 10.1002/minf.202300183] [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: 08/21/2023] [Revised: 01/16/2024] [Accepted: 01/22/2024] [Indexed: 01/24/2024]
Abstract
De novo design has been a hotly pursued topic for many years. Most recent developments have involved the use of deep learning methods for generative molecular design. Despite increasing levels of algorithmic sophistication, the design of molecules that are synthetically accessible remains a major challenge. Reaction-based de novo design takes a conceptually simpler approach and aims to address synthesisability directly by mimicking synthetic chemistry and driving structural transformations by known reactions that are applied in a stepwise manner. However, the use of a small number of hand-coded transformations restricts the chemical space that can be accessed and there are few examples in the literature where molecules and their synthetic routes have been designed and executed successfully. Here we describe the application of reaction-based de novo design to the design of synthetically accessible and biologically active compounds as proof-of-concept of our reaction vector-based software. Reaction vectors are derived automatically from known reactions and allow access to a wide region of synthetically accessible chemical space. The design was aimed at producing molecules that are active against PARP1 and which have improved brain penetration properties compared to existing PARP1 inhibitors. We synthesised a selection of the designed molecules according to the provided synthetic routes and tested them experimentally. The results demonstrate that reaction vectors can be applied to the design of novel molecules of biological relevance that are also synthetically accessible.
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Affiliation(s)
- Gian Marco Ghiandoni
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
| | - Stuart R Flanagan
- Evotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK
| | - Michael J Bodkin
- Evotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK
| | - Maria Giulia Nizi
- Department of Pharmaceutical Sciences, University of Perugia, 06123, Perugia, Italy
| | - Albert Galera-Prat
- Faculty of Biochemistry and Molecular Medicine & Biocenter Oulu, University of Oulu, Oulu, FI-90014, Finland
| | - Annalaura Brai
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, I-53100, Siena, Italy
| | - Beining Chen
- Department of Chemistry, University of Sheffield, Dainton Building, Brook Hill, Sheffield, S3 7HF, UK
| | - James E A Wallace
- Evotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK
| | - Dimitar Hristozov
- Evotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK
| | - James Webster
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
| | - Giuseppe Manfroni
- Department of Pharmaceutical Sciences, University of Perugia, 06123, Perugia, Italy
| | - Lari Lehtiö
- Faculty of Biochemistry and Molecular Medicine & Biocenter Oulu, University of Oulu, Oulu, FI-90014, Finland
| | - Oriana Tabarrini
- Department of Pharmaceutical Sciences, University of Perugia, 06123, Perugia, Italy
| | - Valerie J Gillet
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
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Ghiandoni GM, Evertsson E, Riley DJ, Tyrchan C, Rathi PC. Augmenting DMTA using predictive AI modelling at AstraZeneca. Drug Discov Today 2024; 29:103945. [PMID: 38460568 DOI: 10.1016/j.drudis.2024.103945] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 02/27/2024] [Accepted: 03/05/2024] [Indexed: 03/11/2024]
Abstract
Design-Make-Test-Analyse (DMTA) is the discovery cycle through which molecules are designed, synthesised, and assayed to produce data that in turn are analysed to inform the next iteration. The process is repeated until viable drug candidates are identified, often requiring many cycles before reaching a sweet spot. The advent of artificial intelligence (AI) and cloud computing presents an opportunity to innovate drug discovery to reduce the number of cycles needed to yield a candidate. Here, we present the Predictive Insight Platform (PIP), a cloud-native modelling platform developed at AstraZeneca. The impact of PIP in each step of DMTA, as well as its architecture, integration, and usage, are discussed and used to provide insights into the future of drug discovery.
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Affiliation(s)
- Gian Marco Ghiandoni
- Augmented DMTA Platform, R&D IT, AstraZeneca, The Discovery Centre (DISC), Francis Crick Avenue, Cambridge CB2 0AA, UK.
| | - Emma Evertsson
- Research and Early Development, Respiratory and Immunology (R&I), Biopharmaceuticals R&D, AstraZeneca, Pepparedsleden, Mölndal, SE 43183, Sweden
| | - David J Riley
- Augmented DMTA Platform, R&D IT, AstraZeneca, The Discovery Centre (DISC), Francis Crick Avenue, Cambridge CB2 0AA, UK
| | - Christian Tyrchan
- Research and Early Development, Respiratory and Immunology (R&I), Biopharmaceuticals R&D, AstraZeneca, Pepparedsleden, Mölndal, SE 43183, Sweden
| | - Prakash Chandra Rathi
- Augmented DMTA Platform, R&D IT, AstraZeneca, The Discovery Centre (DISC), Francis Crick Avenue, Cambridge CB2 0AA, UK
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Frolov AI, Chankeshwara SV, Abdulkarim Z, Ghiandoni GM. pIChemiSt ─ Free Tool for the Calculation of Isoelectric Points of Modified Peptides. J Chem Inf Model 2023; 63:187-196. [PMID: 36573842 PMCID: PMC9832473 DOI: 10.1021/acs.jcim.2c01261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
The isoelectric point (pI) is a fundamental physicochemical property of peptides and proteins. It is widely used to steer design away from low solubility and aggregation and guide peptide separation and purification. Experimental measurements of pI can be replaced by calculations knowing the ionizable groups of peptides and their corresponding pKa values. Different pKa sets are published in the literature for natural amino acids, however, they are insufficient to describe synthetically modified peptides, complex peptides of natural origin, and peptides conjugated with structures of other modalities. Noncanonical modifications (nCAAs) are ignored in the conventional sequence-based pI calculations, therefore producing large errors in their pI predictions. In this work, we describe a pI calculation method that uses the chemical structure as an input, automatically identifies ionizable groups of nCAAs and other fragments, and performs pKa predictions for them. The method is validated on a curated set of experimental measures on 29 modified and 119093 natural peptides, providing an improvement of R2 from 0.74 to 0.95 and 0.96 against the conventional sequence-based approach for modified peptides for the two studied pKa prediction tools, ACDlabs and pKaMatcher, correspondingly. The method is available in the form of an open source Python library at https://github.com/AstraZeneca/peptide-tools, which can be integrated into other proprietary and free software packages. We anticipate that the pI calculation tool may facilitate optimization and purification activities across various application domains of peptides, including the development of biopharmaceuticals.
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Affiliation(s)
- Andrey I. Frolov
- Medicinal
Chemistry, Research and Early Development, Cardiovascular, Renal and
Metabolism (CVRM), BioPharmaceuticals R&D,
AstraZeneca, Gothenburg, Sweden,
| | - Sunay V. Chankeshwara
- Medicinal
Chemistry, Research and Early Development, Cardiovascular, Renal and
Metabolism (CVRM), BioPharmaceuticals R&D,
AstraZeneca, Gothenburg, Sweden
| | - Zeyed Abdulkarim
- Early
Chemical Development, Pharmaceutical Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
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Ghiandoni GM, Bodkin MJ, Chen B, Hristozov D, Wallace JEA, Webster J, Gillet VJ. RENATE: A Pseudo-retrosynthetic Tool for Synthetically Accessible de Novo Design. Mol Inform 2021; 41:e2100207. [PMID: 34750989 PMCID: PMC9285524 DOI: 10.1002/minf.202100207] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 10/23/2021] [Indexed: 11/09/2022]
Abstract
Reaction‐based de novo design refers to the generation of synthetically accessible molecules using transformation rules extracted from known reactions in the literature. In this context, we have previously described the extraction of reaction vectors from a reactions database and their coupling with a structure generation algorithm for the generation of novel molecules from a starting material. An issue when designing molecules from a starting material is the combinatorial explosion of possible product molecules that can be generated, especially for multistep syntheses. Here, we present the development of RENATE, a reaction‐based de novo design tool, which is based on a pseudo‐retrosynthetic fragmentation of a reference ligand and an inside‐out approach to de novo design. The reference ligand is fragmented; each fragment is used to search for similar fragments as building blocks; the building blocks are combined into products using reaction vectors; and a synthetic route is suggested for each product molecule. The RENATE methodology is presented followed by a retrospective validation to recreate a set of approved drugs. Results show that RENATE can generate very similar or even identical structures to the corresponding input drugs, hence validating the fragmentation, search, and design heuristics implemented in the tool.
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Affiliation(s)
- Gian Marco Ghiandoni
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
| | | | - Beining Chen
- Chemistry Department, University of Sheffield, Dainton Building, Brook Hill, Sheffield, S3 7HF, UK
| | | | | | - James Webster
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
| | - Valerie J Gillet
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
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Ghiandoni GM, Bodkin MJ, Chen B, Hristozov D, Wallace JEA, Webster J, Gillet VJ. Enhancing reaction-based de novo design using a multi-label reaction class recommender. J Comput Aided Mol Des 2020; 34:783-803. [PMID: 32112286 PMCID: PMC7293200 DOI: 10.1007/s10822-020-00300-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2019] [Accepted: 02/13/2020] [Indexed: 12/31/2022]
Abstract
Reaction-based de novo design refers to the in-silico generation of novel chemical structures by combining reagents using structural transformations derived from known reactions. The driver for using reaction-based transformations is to increase the likelihood of the designed molecules being synthetically accessible. We have previously described a reaction-based de novo design method based on reaction vectors which are transformation rules that are encoded automatically from reaction databases. A limitation of reaction vectors is that they account for structural changes that occur at the core of a reaction only, and they do not consider the presence of competing functionalities that can compromise the reaction outcome. Here, we present the development of a Reaction Class Recommender to enhance the reaction vector framework. The recommender is intended to be used as a filter on the reaction vectors that are applied during de novo design to reduce the combinatorial explosion of in-silico molecules produced while limiting the generated structures to those which are most likely to be synthesisable. The recommender has been validated using an external data set extracted from the recent medicinal chemistry literature and in two simulated de novo design experiments. Results suggest that the use of the recommender drastically reduces the number of solutions explored by the algorithm while preserving the chance of finding relevant solutions and increasing the global synthetic accessibility of the designed molecules.
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Affiliation(s)
- Gian Marco Ghiandoni
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
| | - Michael J Bodkin
- Evotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK
| | - Beining Chen
- Chemistry Department, University of Sheffield, Dainton Building, Brook Hill, Sheffield, S3 7HF, UK
| | - Dimitar Hristozov
- Evotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK
| | - James E A Wallace
- Evotec (U.K.) Ltd, 114 Innovation Drive, Milton Park, Abingdon, OX14 4RZ, UK
| | - James Webster
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK
| | - Valerie J Gillet
- Information School, University of Sheffield, Regent Court, 211 Portobello, Sheffield, S1 4DP, UK.
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Ghiandoni GM, Bodkin MJ, Chen B, Hristozov D, Wallace JEA, Webster J, Gillet VJ. Development and Application of a Data-Driven Reaction Classification Model: Comparison of an Electronic Lab Notebook and Medicinal Chemistry Literature. J Chem Inf Model 2019; 59:4167-4187. [PMID: 31529948 DOI: 10.1021/acs.jcim.9b00537] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Reaction classification has often been considered an important task for many different applications, and has traditionally been accomplished using hand-coded rule-based approaches. However, the availability of large collections of reactions enables data-driven approaches to be developed. We present the development and validation of a 336-class machine learning-based classification model integrated within a Conformal Prediction (CP) framework to associate reaction class predictions with confidence estimations. We also propose a data-driven approach for "dynamic" reaction fingerprinting to maximize the effectiveness of reaction encoding, as well as developing a novel reaction classification system that organizes labels into four hierarchical levels (SHREC: Sheffield Hierarchical REaction Classification). We show that the performance of the CP augmented model can be improved by defining confidence thresholds to detect predictions that are less likely to be false. For example, the external validation of the model reports 95% of predictions as correct by filtering out less than 15% of the uncertain classifications. The application of the model is demonstrated by classifying two reaction data sets: one extracted from an industrial ELN and the other from the medicinal chemistry literature. We show how confidence estimations and class compositions across different levels of information can be used to gain immediate insights on the nature of reaction collections and hidden relationships between reaction classes.
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Affiliation(s)
- Gian Marco Ghiandoni
- Information School , University of Sheffield , Regent Court, 211 Portobello , Sheffield S1 4DP , United Kingdom
| | - Michael J Bodkin
- Evotec (U.K.) Ltd. , 114 Innovation Drive , Milton Park, Abingdon OX14 4RZ , United Kingdom
| | - Beining Chen
- Chemistry Department , University of Sheffield , Dainton Building , Brook Hill, Sheffield S3 7HF , United Kingdom
| | - Dimitar Hristozov
- Evotec (U.K.) Ltd. , 114 Innovation Drive , Milton Park, Abingdon OX14 4RZ , United Kingdom
| | - James E A Wallace
- Evotec (U.K.) Ltd. , 114 Innovation Drive , Milton Park, Abingdon OX14 4RZ , United Kingdom
| | - James Webster
- Information School , University of Sheffield , Regent Court, 211 Portobello , Sheffield S1 4DP , United Kingdom
| | - Valerie J Gillet
- Information School , University of Sheffield , Regent Court, 211 Portobello , Sheffield S1 4DP , United Kingdom
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