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Chakraborty B, Pal R, Pratihar DK, Chattaraj PK. Bonobo Optimizer: A New Tool Toward the Global Optimization of Small Atomic Clusters. J Phys Chem A 2024. [PMID: 38696762 DOI: 10.1021/acs.jpca.4c02024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/04/2024]
Abstract
In the realm of structural and bonding investigations within chemical systems, elucidating global minimum energy configurations stands as a paramount goal. As the systems increase in size and complexity, this pursuit becomes progressively challenging. Herein, we introduce Bonobo optimizer (BO), a metaheuristic algorithm inspired by the social and reproductive behaviors of bonobos, to the domain of chemical problem solving. Focusing on small carbon clusters, this study systematically evaluates BO's performance, showcasing its robustness and efficiency. Parametric studies highlight the algorithm's adaptability, consistently converging to global minimum structures. Rigorous statistical validation supports the results, and a comparative analysis against established global optimization algorithms underlines BO's superior efficiency. This exploration extends the applicability of BO to the optimization of atomic clusters, providing a promising avenue for future advancements in computational chemistry.
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Affiliation(s)
- Bhrigu Chakraborty
- Department of Chemistry, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | - Ranita Pal
- Advanced Technology Development Centre, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | - Dilip Kumar Pratihar
- Department of Mechanical Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, India
| | - Pratim Kumar Chattaraj
- Department of Chemistry, Birla Institute of Technology, Mesra, Ranchi, Jharkhand 835215, India
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Tripolitsiotis NP, Thomaidi M, Neochoritis CG. The Ugi Three‐Component Reaction; a Valuable Tool in Modern Organic Synthesis. European J Org Chem 2020. [DOI: 10.1002/ejoc.202001157] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
| | - Maria Thomaidi
- Chemistry Department School of Science and Engineering University of Crete 70013 Heraklion Greece
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Affiliation(s)
- Marco Foscato
- Department of Chemistry, University of Bergen, Allégaten 41, N-5007 Bergen, Norway
| | - Vidar R. Jensen
- Department of Chemistry, University of Bergen, Allégaten 41, N-5007 Bergen, Norway
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Machine learning for target discovery in drug development. Curr Opin Chem Biol 2019; 56:16-22. [PMID: 31734566 DOI: 10.1016/j.cbpa.2019.10.003] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2019] [Revised: 10/01/2019] [Accepted: 10/03/2019] [Indexed: 12/15/2022]
Abstract
The discovery of macromolecular targets for bioactive agents is currently a bottleneck for the informed design of chemical probes and drug leads. Typically, activity profiling against genetically manipulated cell lines or chemical proteomics is pursued to shed light on their biology and deconvolute drug-target networks. By taking advantage of the ever-growing wealth of publicly available bioactivity data, learning algorithms now provide an attractive means to generate statistically motivated research hypotheses and thereby prioritize biochemical screens. Here, we highlight recent successes in machine intelligence for target identification and discuss challenges and opportunities for drug discovery.
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Fan B, Li Y, Bi Y, Tong L, Li D, Wang Y. Design of multi-drug combinations for poly-pharmacological effects using composition-activity relationship modeling and multi-objective optimization approach: Application in traditional Chinese medicine. Chem Biol Drug Des 2018; 93:1073-1082. [PMID: 30230238 DOI: 10.1111/cbdd.13396] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2018] [Revised: 07/26/2018] [Accepted: 08/26/2018] [Indexed: 12/21/2022]
Abstract
In recent years, multi-component therapies are increasingly utilized to treat complex diseases such as cancer, diabetes, and other chronic complex diseases. Here, we proposed the protocol for rational design of drug combination with poly-pharmacological effects by integration of design of experiments (DOE), computational modeling, and multiple-objective optimization algorithm. Here, we introduce a common workflow for modeling quantitative relationship of chemical composition and multiple activities of drug combinations. As an example, anti-oxidation, neuroprotective, and anti-platelet activities of three different salvia polyphenols were measured, which were mathematically represented by multivariant regression models to evaluate the effect of combination. In validation, the optimized combination which obtained by weighed-sum method showed good activities in all three models. Our results demonstrate that the multiple-objective optimization strategy is suitable to optimize the ratio of the compounds so to induce the best therapeutic action.
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Affiliation(s)
- Bo Fan
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Yunfei Li
- State Key Laboratory of Core Technology in Innovative Chinese Medicine, Tasly Academy, Tasly Holding Group Co., Ltd., Tianjin, China
| | - Yong Bi
- Department of Neurology, Shanghai Fourth People's Hospital, Shanghai, China
| | - Ling Tong
- State Key Laboratory of Core Technology in Innovative Chinese Medicine, Tasly Academy, Tasly Holding Group Co., Ltd., Tianjin, China
| | - Dongxiang Li
- State Key Laboratory of Core Technology in Innovative Chinese Medicine, Tasly Academy, Tasly Holding Group Co., Ltd., Tianjin, China
| | - Yi Wang
- Pharmaceutical Informatics Institute, College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
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Li Y, Zhang L, Liu Z. Multi-objective de novo drug design with conditional graph generative model. J Cheminform 2018; 10:33. [PMID: 30043127 PMCID: PMC6057868 DOI: 10.1186/s13321-018-0287-6] [Citation(s) in RCA: 138] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Accepted: 07/13/2018] [Indexed: 12/31/2022] Open
Abstract
Recently, deep generative models have revealed itself as a promising way of performing de novo molecule design. However, previous research has focused mainly on generating SMILES strings instead of molecular graphs. Although available, current graph generative models are are often too general and computationally expensive. In this work, a new de novo molecular design framework is proposed based on a type of sequential graph generators that do not use atom level recurrent units. Compared with previous graph generative models, the proposed method is much more tuned for molecule generation and has been scaled up to cover significantly larger molecules in the ChEMBL database. It is shown that the graph-based model outperforms SMILES based models in a variety of metrics, especially in the rate of valid outputs. For the application of drug design tasks, conditional graph generative model is employed. This method offers highe flexibility and is suitable for generation based on multiple objectives. The results have demonstrated that this approach can be effectively applied to solve several drug design problems, including the generation of compounds containing a given scaffold, compounds with specific drug-likeness and synthetic accessibility requirements, as well as dual inhibitors against JNK3 and GSK-3β.
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Affiliation(s)
- Yibo Li
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Xueyuan Road 38, Haidian District, Beijing, 100191, China
| | - Liangren Zhang
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Xueyuan Road 38, Haidian District, Beijing, 100191, China.
| | - Zhenming Liu
- State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, Xueyuan Road 38, Haidian District, Beijing, 100191, China.
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Abstract
Small-molecule drug discovery can be viewed as a challenging multidimensional problem in which various characteristics of compounds - including efficacy, pharmacokinetics and safety - need to be optimized in parallel to provide drug candidates. Recent advances in areas such as microfluidics-assisted chemical synthesis and biological testing, as well as artificial intelligence systems that improve a design hypothesis through feedback analysis, are now providing a basis for the introduction of greater automation into aspects of this process. This could potentially accelerate time frames for compound discovery and optimization and enable more effective searches of chemical space. However, such approaches also raise considerable conceptual, technical and organizational challenges, as well as scepticism about the current hype around them. This article aims to identify the approaches and technologies that could be implemented robustly by medicinal chemists in the near future and to critically analyse the opportunities and challenges for their more widespread application.
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Angelina E, Andujar S, Moreno L, Garibotto F, Párraga J, Peruchena N, Cabedo N, Villecco M, Cortes D, Enriz RD. 3-Chlorotyramine Acting as Ligand of the D2
Dopamine Receptor. Molecular Modeling, Synthesis and D2
Receptor Affinity. Mol Inform 2014; 34:28-43. [DOI: 10.1002/minf.201400093] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2014] [Accepted: 09/04/2014] [Indexed: 12/12/2022]
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Lin YC, Hiss JA, Schneider P, Thelesklaf P, Lim YF, Pillong M, Koehler FM, Dittrich PS, Halin C, Wessler S, Schneider G. Piloting the membranolytic activities of peptides with a self-organizing map. Chembiochem 2014; 15:2225-31. [PMID: 25204788 DOI: 10.1002/cbic.201402231] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2014] [Indexed: 01/01/2023]
Abstract
Antimicrobial peptides (AMPs) show remarkable selectivity toward lipid membranes and possess promising antibiotic potential. Their modes of action are diverse and not fully understood, and innovative peptide design strategies are needed to generate AMPs with improved properties. We present a de novo peptide design approach that resulted in new AMPs possessing low-nanomolar membranolytic activities. Thermal analysis revealed an entropy-driven mechanism of action. The study demonstrates sustained potential of advanced computational methods for designing peptides with the desired activity.
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Affiliation(s)
- Yen-Chu Lin
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093 Zürich (Switzerland)
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Abstract
The computer-assisted generation of new chemical entities (NCEs) has matured into solid technology supporting early drug discovery. Both ligand- and receptor-based methods are increasingly used for designing small lead- and druglike molecules with anticipated multi-target activities. Advanced "polypharmacology" prediction tools are essential pillars of these endeavors. In addition, it has been realized that iterative design-synthesis-test cycles facilitate the rapid identification of NCEs with the desired activity profile. Lab-on-a-chip platforms integrating synthesis, analytics and bioactivity determination and controlled by adaptive, chemistry-driven de novo design software will play an important role for future drug discovery.
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Affiliation(s)
- Gisbert Schneider
- Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir-Prelog-Weg 4, 8093 Zürich, Switzerland.
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Reutlinger M, Rodrigues T, Schneider P, Schneider G. Mehrdimensionales De-novo-Moleküldesign durch adaptive Fragmentauswahl. Angew Chem Int Ed Engl 2014. [DOI: 10.1002/ange.201310864] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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Reutlinger M, Rodrigues T, Schneider P, Schneider G. Multi-objective molecular de novo design by adaptive fragment prioritization. Angew Chem Int Ed Engl 2014; 53:4244-8. [PMID: 24623390 DOI: 10.1002/anie.201310864] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2013] [Indexed: 11/11/2022]
Abstract
We present the development and application of a computational molecular de novo design method for obtaining bioactive compounds with desired on- and off-target binding. The approach translates the nature-inspired concept of ant colony optimization to combinatorial building block selection. By relying on publicly available structure-activity data, we developed a predictive quantitative polypharmacology model for 640 human drug targets. By taking reductive amination as an example of a privileged reaction, we obtained novel subtype-selective and multitarget-modulating dopamine D4 antagonists, as well as ligands selective for the sigma-1 receptor with accurately predicted affinities. The nanomolar potencies of the hits obtained, their high ligand efficiencies, and an overall success rate of 90 % demonstrate that this ligand-based computer-aided molecular design method may guide target-focused combinatorial chemistry.
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Affiliation(s)
- Michael Reutlinger
- Eidgenössische Technische Hochschule (ETH), Departement Chemie und Angewandte Biowissenschaften, Vladimir-Prelog-Weg 4, 8093 Zürich (Switzerland)
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