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Qin T, Wang Y, Kong M, Zhong H, Wu T, Xi Z, Qian Z, Li K, Cai Y, Wu J, Li W. Identification of potential PIM-2 inhibitors via ligand-based generative models, molecular docking and molecular dynamics simulations. Mol Divers 2024:10.1007/s11030-024-10916-7. [PMID: 38954072 DOI: 10.1007/s11030-024-10916-7] [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: 04/01/2024] [Accepted: 06/11/2024] [Indexed: 07/04/2024]
Abstract
Proviral Integrations of Moloney-2 (PIM-2) kinase is a promising target for various cancers and other diseases, and its inhibitors hold potential for treating related diseases. However, there is currently no clinically available PIM-2 inhibitor. In this study, we constructed a generative model for de novo PIM-2 inhibitor design based on artificial intelligence, performed molecular docking and molecular dynamics (MD) simulations to develop an efficient PIM-2 inhibitor generative model and discover potential PIM-2 inhibitors. First, we designed a generative model based on a Bi-directional Long Short-Term Memory (BiLSTM) framework combined with a transfer learning strategy and generated a new PIM-2 small molecule library using existing active drug databases. The generated compound library was then virtually screened by molecular docking and scaffold similarity comparison, identifying 10 initial hit compounds with better performance. Next, using the inhibitor in the crystal structure as a positive control, we performed two rounds of MD simulations, with lengths of 100 ns and 500 ns, respectively, to study the dynamic stability of the protein-ligand systems of the 10 compounds with PIM-2. Analyzed the interactions with key hinge region residues, binding free energies, and changes in the ATP pocket size. The generative model demonstrates good molecular generation capability and can generate efficient novel molecules with similar physicochemical properties as active PIM-2 drugs. Among the 10 initially selected hit compounds, 5 compounds C3 (- 29.69 kcal/mol), C4 (- 33.31 kcal/mol), C5 (- 28.59 kcal/mol), C8 (- 34.68 kcal/mol), and C9 (- 25.88 kcal/mol) have higher binding energies with PIM-2 than the positive drug 3YR (- 26.18 kcal/mol). The MD simulation results are consistent with the docking analysis, these compounds have lower and more stable RMSD values for the complex systems with the reported positive drug 3YR and PIM-2 complex system. They can form long-term stable interactions with active site and the hinge region of PIM-2, which suggests these compounds are likely to have potent inhibitory effects on PIM-2. This study provides an efficient generative model for PIM-2 inhibitor research and discovers 5 potential novel PIM-2 inhibitors.
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Affiliation(s)
- Tianli Qin
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
- The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China
| | - Yijian Wang
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Miaomiao Kong
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Hongliang Zhong
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, 325000, Zhejiang, China
| | - Tao Wu
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Zixuan Xi
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
| | - Zhenyong Qian
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, 325000, Zhejiang, China
| | - Ke Li
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, 325000, Zhejiang, China
| | - Yuepiao Cai
- School of Pharmaceutical Sciences, Wenzhou Medical University, Wenzhou, 325000, China.
| | - Jianzhang Wu
- The Eye Hospital, School of Ophthalmology & Optometry, Wenzhou Medical University, Wenzhou, 325027, China.
- Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou, 325000, Zhejiang, China.
| | - Wulan Li
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, China.
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Wang S, Liang D, Wang J, Dong K, Zhang Y, Liang H, Xu X, Song T. FraHMT: A Fragment-Oriented Heterogeneous Graph Molecular Generation Model for Target Proteins. J Chem Inf Model 2024; 64:3718-3732. [PMID: 38644797 DOI: 10.1021/acs.jcim.4c00252] [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: 04/23/2024]
Abstract
The molecular generation task stands as a pivotal step in the domains of computational chemistry and drug discovery, aiming to computationally generate molecular structures for specific properties. In contrast to previous models that focused primarily on SMILES strings or molecular graphs, our model placed a special emphasis on the substructure information on molecules, enabling the model to learn richer chemical rules and structure features from fragments and chemical reaction information on molecules. To accomplish this, we fragmented the molecules to construct heterogeneous graph representations based on atom and fragment information. Then our model mapped the heterogeneous graph data into a latent vector space by using an encoder and employed a self-regressive generative model as a decoder for molecular generation. Additionally, we performed transfer learning on the model using a small set of ligand molecules known to be active against the target protein to generate molecules that bind better to the target protein. Experimental results demonstrate that our model is highly competitive with state-of-the-art models. It can generate valid and diverse molecules with favorable physicochemical properties and drug-likeness. Importantly, they produce novel molecules with high docking scores against the target proteins.
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Affiliation(s)
- Shuang Wang
- College of Computer Science and Technology, China University of Petroleum, QingDao 266580, China
| | - Dingming Liang
- College of Computer Science and Technology, China University of Petroleum, QingDao 266580, China
| | - Jianmin Wang
- College of Computer Science and Technology, China University of Petroleum, QingDao 266580, China
- The Interdisciplinary Graduate Program in Integrative Biotechnology, Yonsei University, Incheon 21983, Republic of Korea
| | - Kaiyu Dong
- College of Computer Science and Technology, China University of Petroleum, QingDao 266580, China
| | - Yunjing Zhang
- College of Computer Science and Technology, China University of Petroleum, QingDao 266580, China
| | - Huicong Liang
- Marine Biomedical Institute of Qingdao, School of Medicine and Pharmacy, Ocean University of China, QingDao 266580, China
| | - Ximing Xu
- Marine Biomedical Institute of Qingdao, School of Medicine and Pharmacy, Ocean University of China, QingDao 266580, China
| | - Tao Song
- College of Computer Science and Technology, China University of Petroleum, QingDao 266580, China
- Department of Artificial Intelligence, Faculty of Computer Science, Polytechnical University of Madrid, Madrid 28031, Spain
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Fan W, He Y, Zhu F. RM-GPT: Enhance the comprehensive generative ability of molecular GPT model via LocalRNN and RealFormer. Artif Intell Med 2024; 150:102827. [PMID: 38553166 DOI: 10.1016/j.artmed.2024.102827] [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: 06/30/2023] [Revised: 02/26/2024] [Accepted: 02/26/2024] [Indexed: 04/02/2024]
Abstract
Due to the surging of cost, artificial intelligence-assisted de novo drug design has supplanted conventional methods and become an emerging option for drug discovery. Although there have arisen many successful examples of applying generative models to the molecular field, these methods struggle to deal with conditional generation that meet chemists' practical requirements which ask for a controllable process to generate new molecules or optimize basic molecules with appointed conditions. To address this problem, a Recurrent Molecular-Generative Pretrained Transformer model is proposed, supplemented by LocalRNN and Residual Attention Layer Transformer, referred to as RM-GPT. RM-GPT rebuilds GPT model's architecture by incorporating LocalRNN and Residual Attention Layer Transformer so that it is able to extract local information and build connectivity between attention blocks. The incorporation of Transformer in these two modules enables leveraging the parallel computing advantages of multi-head attention mechanisms while extracting local structural information effectively. Through exploring and learning in a large chemical space, RM-GPT absorbs the ability to generate drug-like molecules with conditions in demand, such as desired properties and scaffolds, precisely and stably. RM-GPT achieved better results than SOTA methods on conditional generation.
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Affiliation(s)
- Wenfeng Fan
- School of Computer Science and Technology, Soochow University, Suzhou, 215006, China.
| | - Yue He
- School of Computer Science and Technology, Soochow University, Suzhou, 215006, China.
| | - Fei Zhu
- School of Computer Science and Technology, Soochow University, Suzhou, 215006, China.
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Qin R, Zhang H, Huang W, Shao Z, Lei J. Deep learning-based design and screening of benzimidazole-pyrazine derivatives as adenosine A 2B receptor antagonists. J Biomol Struct Dyn 2023:1-17. [PMID: 38133953 DOI: 10.1080/07391102.2023.2295974] [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: 09/16/2023] [Accepted: 12/11/2023] [Indexed: 12/24/2023]
Abstract
The Adenosine A2B receptor (A2BAR) is considered a novel potential target for the immunotherapy of cancer, and A2BAR antagonists have an inhibitory effect on tumor growth, proliferation, and metastasis. In our previous studies, we identified a class of benzimidazole-pyrazine scaffolds whose derivatives exhibited the antagonistic effect but lacked subtype selectivity towards A2BAR. In this work, we developed a scaffold-based protocol that incorporates a deep generative model and multilayer virtual screening to design benzimidazole-pyrazine derivatives as potential selective A2BAR antagonists. By utilizing a generative model with reported A2BAR antagonists as the training set, we built up a scaffold-focused library of benzimidazole-pyrazine derivatives and processed a virtual screening protocol to discover potential A2BAR antagonists. Finally, five molecules with different Bemis-Murcko scaffolds were identified and exhibited higher binding free energies than the reference molecule 12o. Further computational analysis revealed that the 3-benzyl derivative ABA-1266 presented high selectivity toward A2BAR and showed preferred draggability, providing future potent development of selective A2BAR antagonists.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Rui Qin
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Hao Zhang
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
| | - Weifeng Huang
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
| | - Zhenglin Shao
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
| | - Jinping Lei
- School of Pharmaceutical Sciences, Sun Yat-sen University, Guangzhou, China
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