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Kengkanna A, Ohue M. Enhancing property and activity prediction and interpretation using multiple molecular graph representations with MMGX. Commun Chem 2024; 7:74. [PMID: 38580841 PMCID: PMC10997661 DOI: 10.1038/s42004-024-01155-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Accepted: 03/18/2024] [Indexed: 04/07/2024] Open
Abstract
Graph Neural Networks (GNNs) excel in compound property and activity prediction, but the choice of molecular graph representations significantly influences model learning and interpretation. While atom-level molecular graphs resemble natural topology, they overlook key substructures or functional groups and their interpretation partially aligns with chemical intuition. Recent research suggests alternative representations using reduced molecular graphs to integrate higher-level chemical information and leverages both representations for model. However, there is a lack of studies about applicability and impact of different molecular graphs on model learning and interpretation. Here, we introduce MMGX (Multiple Molecular Graph eXplainable discovery), investigating the effects of multiple molecular graphs, including Atom, Pharmacophore, JunctionTree, and FunctionalGroup, on model learning and interpretation with various perspectives. Our findings indicate that multiple graphs relatively improve model performance, but in varying degrees depending on datasets. Interpretation from multiple graphs in different views provides more comprehensive features and potential substructures consistent with background knowledge. These results help to understand model decisions and offer valuable insights for subsequent tasks. The concept of multiple molecular graph representations and diverse interpretation perspectives has broad applicability across tasks, architectures, and explanation techniques, enhancing model learning and interpretation for relevant applications in drug discovery.
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Affiliation(s)
- Apakorn Kengkanna
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, Kanagawa, 226-8501, Japan
| | - Masahito Ohue
- Department of Computer Science, School of Computing, Tokyo Institute of Technology, Kanagawa, 226-8501, Japan.
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2
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Acharya A, Yadav M, Nagpure M, Kumaresan S, Guchhait SK. Molecular medicinal insights into scaffold hopping-based drug discovery success. Drug Discov Today 2024; 29:103845. [PMID: 38013043 DOI: 10.1016/j.drudis.2023.103845] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 11/17/2023] [Accepted: 11/22/2023] [Indexed: 11/29/2023]
Abstract
In both academia and the pharmaceutical industry, innovative hypotheses, methodologies and technologies that can shorten the drug research and development, leading to higher success rates, are vital. In this review, we demonstrate how innovative variations of the scaffold-hopping strategy have been used to create new druggable molecular spaces, drugs, clinical candidates, preclinical candidates, and bioactive agents. We also analyze molecular modulations that enabled improvements of the pharmacodynamic (PD), physiochemical, and pharmacokinetic (PK) properties (P3 properties) of the drugs resulting from these scaffold-hopping strategies.
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Affiliation(s)
- Ayan Acharya
- National Institute of Pharmaceutical Education and Research (NIPER), S.A.S. Nagar, Punjab 160062, India
| | - Mukul Yadav
- National Institute of Pharmaceutical Education and Research (NIPER), S.A.S. Nagar, Punjab 160062, India
| | - Mithilesh Nagpure
- National Institute of Pharmaceutical Education and Research (NIPER), S.A.S. Nagar, Punjab 160062, India
| | - Sanathanalaxmi Kumaresan
- National Institute of Pharmaceutical Education and Research (NIPER), S.A.S. Nagar, Punjab 160062, India; National Institute of Pharmaceutical Education and Research (NIPER), S.A.S. Nagar, Punjab 160062, India
| | - Sankar K Guchhait
- National Institute of Pharmaceutical Education and Research (NIPER), S.A.S. Nagar, Punjab 160062, India.
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Zhu H, Zhou R, Cao D, Tang J, Li M. A pharmacophore-guided deep learning approach for bioactive molecular generation. Nat Commun 2023; 14:6234. [PMID: 37803000 PMCID: PMC10558534 DOI: 10.1038/s41467-023-41454-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2022] [Accepted: 08/30/2023] [Indexed: 10/08/2023] Open
Abstract
The rational design of novel molecules with the desired bioactivity is a critical but challenging task in drug discovery, especially when treating a novel target family or understudied targets. We propose a Pharmacophore-Guided deep learning approach for bioactive Molecule Generation (PGMG). Through the guidance of pharmacophore, PGMG provides a flexible strategy for generating bioactive molecules. PGMG uses a graph neural network to encode spatially distributed chemical features and a transformer decoder to generate molecules. A latent variable is introduced to solve the many-to-many mapping between pharmacophores and molecules to improve the diversity of the generated molecules. Compared to existing methods, PGMG generates molecules with strong docking affinities and high scores of validity, uniqueness, and novelty. In the case studies, we use PGMG in a ligand-based and structure-based drug de novo design. Overall, the flexibility and effectiveness make PGMG a useful tool to accelerate the drug discovery process.
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Affiliation(s)
- Huimin Zhu
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Renyi Zhou
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China
| | - Dongsheng Cao
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha, 410008, China
| | - Jing Tang
- Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, 00290, Finland
- Department of Biochemistry and Developmental Biology, Faculty of Medicine, University of Helsinki, Helsinki, 00290, Finland
| | - Min Li
- School of Computer Science and Engineering, Central South University, Changsha, 410083, China.
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Pinel P, Guichaoua G, Najm M, Labouille S, Drizard N, Gaston-Mathé Y, Hoffmann B, Stoven V. Exploring isofunctional molecules: Design of a benchmark and evaluation of prediction performance. Mol Inform 2023; 42:e2200216. [PMID: 36633361 DOI: 10.1002/minf.202200216] [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: 08/30/2022] [Revised: 12/19/2022] [Accepted: 01/11/2023] [Indexed: 01/13/2023]
Abstract
Identification of novel chemotypes with biological activity similar to a known active molecule is an important challenge in drug discovery called 'scaffold hopping'. Small-, medium-, and large-step scaffold hopping efforts may lead to increasing degrees of chemical structure novelty with respect to the parent compound. In the present paper, we focus on the problem of large-step scaffold hopping. We assembled a high quality and well characterized dataset of scaffold hopping examples comprising pairs of active molecules and including a variety of protein targets. This dataset was used to build a benchmark corresponding to the setting of real-life applications: one active molecule is known, and the second active is searched among a set of decoys chosen in a way to avoid statistical bias. This allowed us to evaluate the performance of computational methods for solving large-step scaffold hopping problems. In particular, we assessed how difficult these problems are, particularly for classical 2D and 3D ligand-based methods. We also showed that a machine-learning chemogenomic algorithm outperforms classical methods and we provided some useful hints for future improvements.
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Affiliation(s)
- Philippe Pinel
- Center for Computational Biology, Mines Paris-PSL, PSL Research University, 75006, Paris, France.,Institut Curie, 75248, Paris, France.,INSERM U900, 75428, Paris, France.,Iktos SAS, 75017, Paris, France
| | - Gwenn Guichaoua
- Center for Computational Biology, Mines Paris-PSL, PSL Research University, 75006, Paris, France.,Institut Curie, 75248, Paris, France.,INSERM U900, 75428, Paris, France
| | - Matthieu Najm
- Center for Computational Biology, Mines Paris-PSL, PSL Research University, 75006, Paris, France.,Institut Curie, 75248, Paris, France.,INSERM U900, 75428, Paris, France
| | | | | | | | | | - Véronique Stoven
- Center for Computational Biology, Mines Paris-PSL, PSL Research University, 75006, Paris, France.,Institut Curie, 75248, Paris, France.,INSERM U900, 75428, Paris, France
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Nakano H, Miyao T. Visualization of Topological Pharmacophore Space with Graph Edit Distance. ACS OMEGA 2022; 7:14057-14068. [PMID: 35559135 PMCID: PMC9088954 DOI: 10.1021/acsomega.2c00173] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 03/25/2022] [Indexed: 06/15/2023]
Abstract
A topological pharmacophore (TP) is a chemical graph-based pharmacophore representation, where nodes are pharmacophoric features (PF) and edges are topological distances between PFs. Previously proposed sparse pharmacophore graphs (SPhGs) for TPs were shown to be effective in identifying structurally different active compounds while maintaining the interpretability of the graphs. However, one limitation of using SPhGs as queries is that many structurally similar SPhGs can be identified from a set of active compounds, requiring the classification and visualization of SPhGs, followed by an understanding of the pharmacophore hypotheses. In this study, we propose a scheme for SPhG analysis based on dimensionality reduction techniques with the graph edit distance (GED) metric. This metric enables measuring similarities among SPhGs in a quantitative manner. The visualization of SPhGs, which themselves are the graphs shared by active compounds, can help us understand the pharmacophore hypotheses as well as the data set. As a proof-of-concept study, we generated two-dimensional SPhG-maps using three dimensionality reduction techniques for six biological targets. A comparison with other pharmacophore representations was also conducted. We demonstrated knowledge extraction (interpretation of the data set) from the generated maps. Our findings include a suitable mapping algorithm as well as a pharmacophore hypothesis analysis procedure using an SPhG-map.
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Affiliation(s)
- Hiroshi Nakano
- Graduate
School of Science and Technology, Nara Institute
of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan
| | - Tomoyuki Miyao
- Graduate
School of Science and Technology, Nara Institute
of Science and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan
- Data
Science Center, Nara Institute of Science
and Technology, 8916-5 Takayama-cho, Ikoma, Nara 630-0192, Japan
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