1
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Weller JA, Rohs R. Structure-Based Drug Design with a Deep Hierarchical Generative Model. J Chem Inf Model 2024; 64:6450-6463. [PMID: 39058534 DOI: 10.1021/acs.jcim.4c01193] [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: 07/28/2024]
Abstract
Recently, the remarkable growth of available crystal structure data and libraries of commercially available or readily synthesizable molecules have unlocked previously inaccessible regions of chemical space for drug development. Paired with improvements in virtual ligand screening methods, these expanded libraries are having a notable impact on early drug design efforts. Yet screening-based methods still face scalability limits, due to computational constraints and the sheer scale of drug-like space. Machine learning approaches are overcoming these limitations by learning the fundamental intra- and intermolecular relationships in drug-target systems from existing data. Here, we introduce DrugHIVE, a deep hierarchical variational autoencoder that outperforms state-of-the-art autoregressive and diffusion-based methods in both speed and performance on common generative benchmarks. DrugHIVE's hierarchical design enables improved control over molecular generation. Its capabilities include dramatically increasing virtual screening efficiency and accelerating a wide range of common drug design tasks, including de novo generation, molecular optimization, scaffold hopping, linker design, and high-throughput pattern replacement. Our highly scalable method can even be applied to receptors with high-confidence AlphaFold-predicted structures, extending the ability to generate high-quality drug-like molecules to a majority of the unsolved human proteome.
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Affiliation(s)
- Jesse A Weller
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California 90089, United States
- Department of Physics and Astronomy, University of Southern California, Los Angeles, California 90089, United States
| | - Remo Rohs
- Department of Quantitative and Computational Biology, University of Southern California, Los Angeles, California 90089, United States
- Department of Physics and Astronomy, University of Southern California, Los Angeles, California 90089, United States
- Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States
- Thomas Lord Department of Computer Science, University of Southern California, Los Angeles, California 90089, United States
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2
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Zhao M, Yu W, MacKerell AD. Enhancing SILCS-MC via GPU Acceleration and Ligand Conformational Optimization with Genetic and Parallel Tempering Algorithms. J Phys Chem B 2024; 128:7362-7375. [PMID: 39031121 PMCID: PMC11294009 DOI: 10.1021/acs.jpcb.4c03045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2024]
Abstract
In the domain of computer-aided drug design, achieving precise and accurate estimates of ligand-protein binding is paramount in the context of screening extensive drug libraries and performing ligand optimization. A fundamental aspect of the SILCS (site identification by ligand competitive saturation) methodology lies in the generation of comprehensive 3D free-energy functional group affinity maps (FragMaps), encompassing the entirety of the target molecule structure. These FragMaps offer an intricate landscape of functional group affinities across the protein, bilayer, or RNA, acting as the basis for subsequent SILCS-Monte Carlo (MC) simulations wherein ligands are docked to the target molecule. To augment the efficiency and breadth of ligand sampling capabilities, we implemented an improved SILCS-MC methodology. By harnessing the parallel computing capability of GPUs, our approach facilitates concurrent calculations over multiple ligands and binding sites, markedly enhancing the computational efficiency. Moreover, the integration of a genetic algorithm (GA) with MC allows us to employ an evolutionary approach to perform ligand sampling, assuring enhanced convergence characteristics. In addition, the potential utility of parallel tempering (PT) to improve sampling was investigated. Implementation of SILCS-MC on GPU architecture is shown to accelerate the speed of SILCS-MC calculations by over 2-orders of magnitude. Use of GA and PT yield improvements over Markov-chain MC, increasing the precision of the resultant docked orientations and binding free energies, though the extent of improvements is relatively small. Accordingly, significant improvements in speed are obtained through the GPU implementation with minor improvements in the precision of the docking obtained via the tested GA and PT algorithms.
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Affiliation(s)
- Mingtian Zhao
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, 20 Penn St., Baltimore, Maryland 21201, USA
| | - Wenbo Yu
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, 20 Penn St., Baltimore, Maryland 21201, USA
| | - Alexander D. MacKerell
- Computer Aided Drug Design Center, Department of Pharmaceutical Sciences, University of Maryland, School of Pharmacy, 20 Penn St., Baltimore, Maryland 21201, USA
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3
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Yoo S, Kim J. Adapt-cMolGPT: A Conditional Generative Pre-Trained Transformer with Adapter-Based Fine-Tuning for Target-Specific Molecular Generation. Int J Mol Sci 2024; 25:6641. [PMID: 38928346 PMCID: PMC11203498 DOI: 10.3390/ijms25126641] [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: 05/16/2024] [Revised: 06/09/2024] [Accepted: 06/14/2024] [Indexed: 06/28/2024] Open
Abstract
Small-molecule drug design aims to generate compounds that target specific proteins, playing a crucial role in the early stages of drug discovery. Recently, research has emerged that utilizes the GPT model, which has achieved significant success in various fields to generate molecular compounds. However, due to the persistent challenge of small datasets in the pharmaceutical field, there has been some degradation in the performance of generating target-specific compounds. To address this issue, we propose an enhanced target-specific drug generation model, Adapt-cMolGPT, which modifies molecular representation and optimizes the fine-tuning process. In particular, we introduce a new fine-tuning method that incorporates an adapter module into a pre-trained base model and alternates weight updates by sections. We evaluated the proposed model through multiple experiments and demonstrated performance improvements compared to previous models. In the experimental results, Adapt-cMolGPT generated a greater number of novel and valid compounds compared to other models, with these generated compounds exhibiting properties similar to those of real molecular data. These results indicate that our proposed method is highly effective in designing drugs targeting specific proteins.
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Affiliation(s)
- Soyoung Yoo
- Department of Artificial Intelligence, Sejong University, Seoul 05006, Republic of Korea;
| | - Junghyun Kim
- Department of Artificial Intelligence, Sejong University, Seoul 05006, Republic of Korea;
- Deep Learning Architecture Research Center, Sejong University, Seoul 05006, Republic of Korea
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4
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Aksamit N, Hou J, Li Y, Ombuki-Berman B. Integrating transformers and many-objective optimization for drug design. BMC Bioinformatics 2024; 25:208. [PMID: 38849719 PMCID: PMC11161990 DOI: 10.1186/s12859-024-05822-6] [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: 04/07/2024] [Accepted: 05/30/2024] [Indexed: 06/09/2024] Open
Abstract
BACKGROUND Drug design is a challenging and important task that requires the generation of novel and effective molecules that can bind to specific protein targets. Artificial intelligence algorithms have recently showed promising potential to expedite the drug design process. However, existing methods adopt multi-objective approaches which limits the number of objectives. RESULTS In this paper, we expand this thread of research from the many-objective perspective, by proposing a novel framework that integrates a latent Transformer-based model for molecular generation, with a drug design system that incorporates absorption, distribution, metabolism, excretion, and toxicity prediction, molecular docking, and many-objective metaheuristics. We compared the performance of two latent Transformer models (ReLSO and FragNet) on a molecular generation task and show that ReLSO outperforms FragNet in terms of reconstruction and latent space organization. We then explored six different many-objective metaheuristics based on evolutionary algorithms and particle swarm optimization on a drug design task involving potential drug candidates to human lysophosphatidic acid receptor 1, a cancer-related protein target. CONCLUSION We show that multi-objective evolutionary algorithm based on dominance and decomposition performs the best in terms of finding molecules that satisfy many objectives, such as high binding affinity and low toxicity, and high drug-likeness. Our framework demonstrates the potential of combining Transformers and many-objective computational intelligence for drug design.
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Affiliation(s)
- Nicholas Aksamit
- Department of Computer Science, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, ON, L2S 3A1, Canada
| | - Jinqiang Hou
- Department of Chemistry, Lakehead University, 955 Oliver Road, Thunder Bay, ON, P7B 5E1, Canada
- Thunder Bay Regional Health Research Institute, 980 Oliver Road, Thunder Bay, ON, P7B 6V4, Canada
| | - Yifeng Li
- Department of Computer Science, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, ON, L2S 3A1, Canada.
- Department of Biological Sciences, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, ON, L2S 3A1, Canada.
| | - Beatrice Ombuki-Berman
- Department of Computer Science, Brock University, 1812 Sir Isaac Brock Way, St. Catharines, ON, L2S 3A1, Canada.
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5
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Chew PY, Joseph JA, Collepardo-Guevara R, Reinhardt A. Aromatic and arginine content drives multiphasic condensation of protein-RNA mixtures. Biophys J 2024; 123:1342-1355. [PMID: 37408305 PMCID: PMC11163273 DOI: 10.1016/j.bpj.2023.06.024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 06/20/2023] [Accepted: 06/30/2023] [Indexed: 07/07/2023] Open
Abstract
Multiphasic architectures are found ubiquitously in biomolecular condensates and are thought to have important implications for the organization of multiple chemical reactions within the same compartment. Many of these multiphasic condensates contain RNA in addition to proteins. Here, we investigate the importance of different interactions in multiphasic condensates comprising two different proteins and RNA using computer simulations with a residue-resolution coarse-grained model of proteins and RNA. We find that in multilayered condensates containing RNA in both phases, protein-RNA interactions dominate, with aromatic residues and arginine forming the key stabilizing interactions. The total aromatic and arginine content of the two proteins must be appreciably different for distinct phases to form, and we show that this difference increases as the system is driven toward greater multiphasicity. Using the trends observed in the different interaction energies of this system, we demonstrate that we can also construct multilayered condensates with RNA preferentially concentrated in one phase. The "rules" identified can thus enable the design of synthetic multiphasic condensates to facilitate further study of their organization and function.
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Affiliation(s)
- Pin Yu Chew
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, United Kingdom
| | - Jerelle A Joseph
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey
| | - Rosana Collepardo-Guevara
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, United Kingdom; Department of Physics, University of Cambridge, Cambridge, United Kingdom; Department of Genetics, University of Cambridge, Cambridge, United Kingdom.
| | - Aleks Reinhardt
- Yusuf Hamied Department of Chemistry, University of Cambridge, Cambridge, United Kingdom.
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6
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Humayun F, Khan F, Khan A, Alshammari A, Ji J, Farhan A, Fawad N, Alam W, Ali A, Wei DQ. De novo generation of dual-target ligands for the treatment of SARS-CoV-2 using deep learning, virtual screening, and molecular dynamic simulations. J Biomol Struct Dyn 2024; 42:3019-3029. [PMID: 37449757 DOI: 10.1080/07391102.2023.2234481] [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: 12/26/2022] [Accepted: 04/30/2023] [Indexed: 07/18/2023]
Abstract
De novo generation of molecules with the necessary features offers a promising opportunity for artificial intelligence, such as deep generative approaches. However, creating novel compounds having biological activities toward two distinct targets continues to be a very challenging task. In this study, we develop a unique computational framework for the de novo synthesis of bioactive compounds directed at two predetermined therapeutic targets. This framework is referred to as the dual-target ligand generative network. Our approach uses a stochastic policy to explore chemical spaces called a sequence-based simple molecular input line entry system (SMILES) generator. The steps in the high-level workflow would be to gather and prepare the training data for both targets' molecules, build a neural network model and train it to make molecules, create new molecules using generative AI, and then virtually screen the newly validated molecules against the SARS-CoV-2 PLpro and 3CLpro drug targets. Results shows that novel molecules generated have higher binding affinity with both targets than the conventional drug i.e. Remdesivir being used for the treatment of SARS-CoV-2.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Fahad Humayun
- Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
| | - Fatima Khan
- National Institute of Health, Islamabad, Pakistan
| | - Abbas Khan
- Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
| | - Abdulrahman Alshammari
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, Riyadh, Saudi Arabia
| | - Jun Ji
- Henan Provincial Engineering and Technology Center of Health Products for Livestock and Poultry, Henan Provincial Engineering and Technology Center of Animal Disease Diagnosis and Integrated Control, Nanyang Normal University, Nanyang, PR China
| | - Ali Farhan
- Department of Chemistry, Chung Yuan Christian University, Taoyuan, Taiwan
| | - Nasim Fawad
- Poultry Research Institute, Rawalpindi, Pakistan
| | - Waheed Alam
- National Institute of Health, Islamabad, Pakistan
| | - Arif Ali
- Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
| | - Dong-Qing Wei
- Department of Bioinformatics and Biological Statistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
- State Key Laboratory of Microbial Metabolism and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, PR China
- Centre for Research in Molecular Modeling, Concordia University, Québec, Canada
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7
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Tang Y, Moretti R, Meiler J. Recent Advances in Automated Structure-Based De Novo Drug Design. J Chem Inf Model 2024; 64:1794-1805. [PMID: 38485516 PMCID: PMC10966644 DOI: 10.1021/acs.jcim.4c00247] [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: 02/11/2024] [Revised: 02/26/2024] [Accepted: 02/29/2024] [Indexed: 03/26/2024]
Abstract
As the number of determined and predicted protein structures and the size of druglike 'make-on-demand' libraries soar, the time-consuming nature of structure-based computer-aided drug design calls for innovative computational algorithms. De novo drug design introduces in silico heuristics to accelerate searching in the vast chemical space. This review focuses on recent advances in structure-based de novo drug design, ranging from conventional fragment-based methods, evolutionary algorithms, and Metropolis Monte Carlo methods to deep generative models. Due to the historical limitation of de novo drug design generating readily available drug-like molecules, we highlight the synthetic accessibility efforts in each category and the benchmarking strategies taken to validate the proposed framework.
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Affiliation(s)
- Yidan Tang
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Rocco Moretti
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240, United States
| | - Jens Meiler
- Department
of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States
- Center
for Structural Biology, Vanderbilt University, Nashville, Tennessee 37240, United States
- Institute
of Drug Discovery, Faculty of Medicine, University of Leipzig, 04103 Leipzig, Germany
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8
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Weller JA, Rohs R. DrugHIVE: Target-specific spatial drug design and optimization with a hierarchical generative model. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.12.22.573155. [PMID: 38187658 PMCID: PMC10769420 DOI: 10.1101/2023.12.22.573155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
Rapid advancement in the computational methods of structure-based drug design has led to their widespread adoption as key tools in the early drug development process. Recently, the remarkable growth of available crystal structure data and libraries of commercially available or readily synthesizable molecules have unlocked previously inaccessible regions of chemical space for drug development. Paired with improvements in virtual ligand screening methods, these expanded libraries are having a significant impact on the success of early drug design efforts. However, screening-based methods are limited in their scalability due to computational limits and the sheer scale of drug-like space. An approach within the quickly evolving field of artificial intelligence (AI), deep generative modeling, is extending the reach of molecular design beyond classical methods by learning the fundamental intra- and inter-molecular relationships in drug-target systems from existing data. In this work we introduce DrugHIVE, a deep hierarchical structure-based generative model that enables fine-grained control over molecular generation. Our model outperforms state of the art autoregressive and diffusion-based methods on common benchmarks and in speed of generation. Here, we demonstrate DrugHIVEs capacity to accelerate a wide range of common drug design tasks such as de novo generation, molecular optimization, scaffold hopping, linker design, and high throughput pattern replacement. Our method is highly scalable and can be applied to high confidence AlphaFold predicted receptors, extending our ability to generate high quality drug-like molecules to a majority of the unsolved human proteome.
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9
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Nigam A, Pollice R, Friederich P, Aspuru-Guzik A. Artificial design of organic emitters via a genetic algorithm enhanced by a deep neural network. Chem Sci 2024; 15:2618-2639. [PMID: 38362419 PMCID: PMC10866360 DOI: 10.1039/d3sc05306g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 01/10/2024] [Indexed: 02/17/2024] Open
Abstract
The design of molecules requires multi-objective optimizations in high-dimensional chemical space with often conflicting target properties. To navigate this space, classical workflows rely on the domain knowledge and creativity of human experts, which can be the bottleneck in high-throughput approaches. Herein, we present an artificial molecular design workflow relying on a genetic algorithm and a deep neural network to find a new family of organic emitters with inverted singlet-triplet gaps and appreciable fluorescence rates. We combine high-throughput virtual screening and inverse design infused with domain knowledge and artificial intelligence to accelerate molecular generation significantly. This enabled us to explore more than 800 000 potential emitter molecules and find more than 10 000 candidates estimated to have inverted singlet-triplet gaps (INVEST) and appreciable fluorescence rates, many of which likely emit blue light. This class of molecules has the potential to realize a new generation of organic light-emitting diodes.
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Affiliation(s)
- AkshatKumar Nigam
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto 80 St. George St Toronto Ontario M5S 3H6 Canada
- Department of Computer Science, University of Toronto 40 St. George St Toronto Ontario M5S 2E4 Canada
| | - Robert Pollice
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto 80 St. George St Toronto Ontario M5S 3H6 Canada
- Department of Computer Science, University of Toronto 40 St. George St Toronto Ontario M5S 2E4 Canada
| | - Pascal Friederich
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto 80 St. George St Toronto Ontario M5S 3H6 Canada
- Department of Computer Science, University of Toronto 40 St. George St Toronto Ontario M5S 2E4 Canada
- Institute of Nanotechnology, Karlsruhe Institute of Technology Hermann-von-Helmholtz-Platz 1 76344 Eggenstein-Leopoldshafen Germany
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology Am Fasanengarten 5 76131 Karlsruhe Germany
| | - Alán Aspuru-Guzik
- Chemical Physics Theory Group, Department of Chemistry, University of Toronto 80 St. George St Toronto Ontario M5S 3H6 Canada
- Department of Computer Science, University of Toronto 40 St. George St Toronto Ontario M5S 2E4 Canada
- Vector Institute for Artificial Intelligence 661 University Ave Suite 710 Toronto Ontario M5G 1M1 Canada
- Department of Chemical Engineering & Applied Chemistry, University of Toronto 200 College St. Ontario M5S 3E5 Canada
- Department of Materials Science & Engineering, University of Toronto, 184 College St. Ontario M5S 3E4 Canada
- Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR) 661 University Ave Toronto Ontario M5G Canada
- Acceleration Consortium Toronto Ontario M5G 3H6 Canada
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10
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Angelo JS, Guedes IA, Barbosa HJC, Dardenne LE. Multi-and many-objective optimization: present and future in de novo drug design. Front Chem 2023; 11:1288626. [PMID: 38192501 PMCID: PMC10773868 DOI: 10.3389/fchem.2023.1288626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 11/27/2023] [Indexed: 01/10/2024] Open
Abstract
de novo Drug Design (dnDD) aims to create new molecules that satisfy multiple conflicting objectives. Since several desired properties can be considered in the optimization process, dnDD is naturally categorized as a many-objective optimization problem (ManyOOP), where more than three objectives must be simultaneously optimized. However, a large number of objectives typically pose several challenges that affect the choice and the design of optimization methodologies. Herein, we cover the application of multi- and many-objective optimization methods, particularly those based on Evolutionary Computation and Machine Learning techniques, to enlighten their potential application in dnDD. Additionally, we comprehensively analyze how molecular properties used in the optimization process are applied as either objectives or constraints to the problem. Finally, we discuss future research in many-objective optimization for dnDD, highlighting two important possible impacts: i) its integration with the development of multi-target approaches to accelerate the discovery of innovative and more efficacious drug therapies and ii) its role as a catalyst for new developments in more fundamental and general methodological frameworks in the field.
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Affiliation(s)
| | | | | | - Laurent E. Dardenne
- Coordenação de Modelagem Computacional, Laboratório Nacional de Computação Científica, Petrópolis, Brazil
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11
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Viswanathan K, Goel M, Laghuvarapu S, Varma G, Priyakumar UD. Streamlining pipeline efficiency: a novel model-agnostic technique for accelerating conditional generative and virtual screening pipelines. Sci Rep 2023; 13:21069. [PMID: 38030689 PMCID: PMC10686981 DOI: 10.1038/s41598-023-42952-y] [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: 03/07/2023] [Accepted: 09/16/2023] [Indexed: 12/01/2023] Open
Abstract
The discovery of potential therapeutic agents for life-threatening diseases has become a significant problem. There is a requirement for fast and accurate methods to identify drug-like molecules that can be used as potential candidates for novel targets. Existing techniques like high-throughput screening and virtual screening are time-consuming and inefficient. Traditional molecule generation pipelines are more efficient than virtual screening but use time-consuming docking software. Such docking functions can be emulated using Machine Learning models with comparable accuracy and faster execution times. However, we find that when pre-trained machine learning models are employed in generative pipelines as oracles, they suffer from model degradation in areas where data is scarce. In this study, we propose an active learning-based model that can be added as a supplement to enhanced molecule generation architectures. The proposed method uses uncertainty sampling on the molecules created by the generator model and dynamically learns as the generator samples molecules from different regions of the chemical space. The proposed framework can generate molecules with high binding affinity with [Formula: see text]a 70% improvement in runtime compared to the baseline model by labeling only [Formula: see text]30% of molecules compared to the baseline oracle.
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Affiliation(s)
- Karthik Viswanathan
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Manan Goel
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Siddhartha Laghuvarapu
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - Girish Varma
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India
| | - U Deva Priyakumar
- Center for Computational Natural Sciences and Bioinformatics, International Institute of Information Technology, Hyderabad, 500032, India.
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12
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da Fonseca AM, Cabongo SQ, Caluaco BJ, Colares RP, Fernandes CFC, Dos Santos HS, de Lima-Neto P, Marinho ES. The search for new efficient inhibitors of SARS-COV-2 through the De novo drug design developed by artificial intelligence. J Biomol Struct Dyn 2023; 41:9890-9906. [PMID: 36420665 DOI: 10.1080/07391102.2022.2148128] [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: 08/11/2022] [Accepted: 11/10/2022] [Indexed: 11/25/2022]
Abstract
The pandemic caused by Sars-CoV-2 is a viral infection that has generated one of the most significant health problems worldwide. Previous studies report the main protease (Mpro) as a potential target for this virus, as it is considered a crucial enzyme in mediating replication and viral transcription. This work presented the construction of new bioactive compounds for possible inhibition. The De novo molecular design of drugs method in the incremental construction of a ligant model within a receptor model was used, producing new structures with the help of artificial intelligence. The research algorithm and the scoring function responsible for predicting orientation and affinity in the molecular target at the time of coupling showed, as a result of the simulation, the compound with the highest bioaffinity value, Hit 998, with the energy of -17.62 kcal/mol, and synthetic viability close to 50%. While hit 1103 presented better synthetic viability (80%), its affinity energy of -10.28 kcal/mol. Both were compared with the reference linker N3, with a binding affinity of -7.5 kcal/mol. ADMET tests demonstrated that simulated compounds have a low risk of metabolic activation and do not exert effective distribution in the CNS, suggesting a pharmacokinetic mechanism based on local action, even with high topological polarity, which resulted in low oral bioavailability. In conclusion, MMGBSA, H-bonds, RMSD, SASA, and RMSF values were also obtained through molecular dynamics to verify the stability of the receptor-ligant complex within the active protein site to seek new therapeutic propositions in the fight against the pandemic.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Aluísio Marques da Fonseca
- Mestrado Acadêmico em Sociobiodiversidades e Tecnologias Sustentáveis - MASTS, Instituto de Engenharias e Desenvolvimento Sustentável, Universidade da Integração Internacional da Lusofonia Afro-Brasileira, Acarape, CE, Brazil
| | - Sadrack Queque Cabongo
- Instituto de Ciências Exatas e da Natureza, Universidade da Integração Internacional da Lusofonia Afro-Brasileira, Acarape, CE, Brazil
| | - Bernardino Joaquim Caluaco
- Instituto de Ciências Exatas e da Natureza, Universidade da Integração Internacional da Lusofonia Afro-Brasileira, Acarape, CE, Brazil
| | - Regilany Paulo Colares
- Instituto de Ciências Exatas e da Natureza, Universidade da Integração Internacional da Lusofonia Afro-Brasileira, Acarape, CE, Brazil
| | | | | | - Pedro de Lima-Neto
- Department of Analytical Chemistry and Physical Chemistry, Science Center, Federal University of Ceara, Fortaleza, CE, Brazil
| | - Emmanuel Silva Marinho
- Grupo de química Teorica e Eletroquimica-GQTE, Universidade Estadual do Ceará, Limoeiro do Norte, CE, Brazil
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13
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Singh SK, King K, Gannett C, Chuong C, Joshi SY, Plate C, Farzeen P, Webb EM, Kunche LK, Weger-Lucarelli J, Lowell AN, Brown AM, Deshmukh SA. Data Driven Computational Design and Experimental Validation of Drugs for Accelerated Mitigation of Pandemic-like Scenarios. J Phys Chem Lett 2023; 14:9490-9499. [PMID: 37850349 DOI: 10.1021/acs.jpclett.3c01749] [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: 10/19/2023]
Abstract
Emerging pathogens are a historic threat to public health and economic stability. Current trial-and-error approaches to identify new therapeutics are often ineffective due to their inefficient exploration of the enormous small molecule design space. Here, we present a data-driven computational framework composed of hybrid evolutionary algorithms for evolving functional groups on existing drugs to improve their binding affinity toward the main protease (Mpro) of SARS-CoV-2. We show that combinations of functional groups and sites are critical to design drugs with improved binding affinity, which can be easily achieved using our framework by exploring a fraction of the available search space. Atomistic simulations and experimental validation elucidate that enhanced and prolonged interactions between functionalized drugs and Mpro residues result in their improved therapeutic value over that of the parental compound. Overall, this novel framework is extremely flexible and has the potential to rapidly design inhibitors for any protein with available crystal structures.
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Affiliation(s)
- Samrendra K Singh
- Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Kelsie King
- Research and Informatics, Virginia Tech, Blacksburg, Virginia 24061, United States
- Interdisciplinary Program in Genetics, Bioinformatics, and Computational Biology, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Cole Gannett
- Department of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, United States
- Center for Emerging, Zoonotic, and Arthropod-borne Pathogens, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Christina Chuong
- Department of Biomedical Sciences and Pathobiology, VA-MD Regional College of Veterinary Medicine, Virginia Tech, Blacksburg, Virginia 24061, United States
- Center for Emerging, Zoonotic, and Arthropod-borne Pathogens, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Soumil Y Joshi
- Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Charles Plate
- Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Parisa Farzeen
- Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Emily M Webb
- Department of Entomology, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Lakshmi Kumar Kunche
- Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - James Weger-Lucarelli
- Department of Biomedical Sciences and Pathobiology, VA-MD Regional College of Veterinary Medicine, Virginia Tech, Blacksburg, Virginia 24061, United States
- Center for Emerging, Zoonotic, and Arthropod-borne Pathogens, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Andrew N Lowell
- Department of Chemistry, Virginia Tech, Blacksburg, Virginia 24061, United States
- Center for Emerging, Zoonotic, and Arthropod-borne Pathogens, Virginia Tech, Blacksburg, Virginia 24061, United States
- Faculty of Health Sciences, Translational Biology, Medicine, and Health, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Anne M Brown
- Research and Informatics, Virginia Tech, Blacksburg, Virginia 24061, United States
- Interdisciplinary Program in Genetics, Bioinformatics, and Computational Biology, Virginia Tech, Blacksburg, Virginia 24061, United States
- Center for Emerging, Zoonotic, and Arthropod-borne Pathogens, Virginia Tech, Blacksburg, Virginia 24061, United States
- Department of Biochemistry, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Sanket A Deshmukh
- Department of Chemical Engineering, Virginia Tech, Blacksburg, Virginia 24061, United States
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14
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Zhong R, Peng F, Zhang E, Yu J, Munetomo M. Vegetation Evolution with Dynamic Maturity Strategy and Diverse Mutation Strategy for Solving Optimization Problems. Biomimetics (Basel) 2023; 8:454. [PMID: 37887585 PMCID: PMC10604831 DOI: 10.3390/biomimetics8060454] [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: 07/19/2023] [Revised: 09/20/2023] [Accepted: 09/20/2023] [Indexed: 10/28/2023] Open
Abstract
We introduce two new search strategies to further improve the performance of vegetation evolution (VEGE) for solving continuous optimization problems. Specifically, the first strategy, named the dynamic maturity strategy, allows individuals with better fitness to have a higher probability of generating more seed individuals. Here, all individuals will first become allocated to generate a fixed number of seeds, and then the remaining number of allocatable seeds will be distributed competitively according to their fitness. Since VEGE performs poorly in getting rid of local optima, we propose the diverse mutation strategy as the second search operator with several different mutation methods to increase the diversity of seed individuals. In other words, each generated seed individual will randomly choose one of the methods to mutate with a lower probability. To evaluate the performances of the two proposed strategies, we run our proposal (VEGE + two strategies), VEGE, and another seven advanced evolutionary algorithms (EAs) on the CEC2013 benchmark functions and seven popular engineering problems. Finally, we analyze the respective contributions of these two strategies to VEGE. The experimental and statistical results confirmed that our proposal can significantly accelerate convergence and improve the convergence accuracy of the conventional VEGE in most optimization problems.
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Affiliation(s)
- Rui Zhong
- Graduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0808, Japan;
| | - Fei Peng
- Graduate School of Science and Technology, Niigata University, Niigata 950-3198, Japan;
| | - Enzhi Zhang
- Graduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0808, Japan;
| | - Jun Yu
- Institute of Science and Technology, Niigata University, Niigata 950-3198, Japan;
| | - Masaharu Munetomo
- Information Initiative Center, Hokkaido University, Sapporo 060-0808, Japan;
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15
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Elhady SS, Alshobaki NM, Elfaky MA, Koshak AE, Alharbi M, Abdelhameed RFA, Darwish KM. Deciphering Molecular Aspects of Potential α-Glucosidase Inhibitors within Aspergillus terreus: A Computational Odyssey of Molecular Docking-Coupled Dynamics Simulations and Pharmacokinetic Profiling. Metabolites 2023; 13:942. [PMID: 37623885 PMCID: PMC10456934 DOI: 10.3390/metabo13080942] [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: 06/30/2023] [Revised: 08/01/2023] [Accepted: 08/08/2023] [Indexed: 08/26/2023] Open
Abstract
Hyperglycemia, as a hallmark of the metabolic malady diabetes mellitus, has been an overwhelming healthcare burden owing to its high rates of comorbidity and mortality, as well as prospective complications affecting different body organs. Available therapeutic agents, with α-glucosidase inhibitors as one of their cornerstone arsenal, control stages of broad glycemia while showing definitive characteristics related to their low clinical efficiency and off-target complications. This has propelled the academia and industrial section into discovering novel and safer candidates. Herein, we provided a thorough computational exploration of identifying candidates from the marine-derived Aspergillus terreus isolates. Combined structural- and ligand-based approaches using a chemical library of 275 metabolites were adopted for pinpointing promising α-glucosidase inhibitors, as well as providing guiding insights for further lead optimization and development. Structure-based virtual screening through escalating precision molecular docking protocol at the α-glucosidase canonical pocket identified 11 promising top-docked hits, with several being superior to the market drug reference, acarbose. Comprehensive ligand-based investigations of these hits' pharmacokinetics ADME profiles, physiochemical characterizations, and obedience to the gold standard Lipinski's rule of five, as well as toxicity and mutagenicity profiling, proceeded. Under explicit conditions, a molecular dynamics simulation identified the top-stable metabolites: butyrolactone VI (SK-44), aspulvinone E (SK-55), butyrolactone I 4''''-sulfate (SK-72), and terrelumamide B (SK-173). They depicted the highest free binding energies and steadiest thermodynamic behavior. Moreover, great structural insights have been revealed, including the advent of an aromatic scaffold-based interaction for ligand-target complex stability. The significance of introducing balanced hydrophobic/polar moieties, like triazole and other bioisosteres of carboxylic acid, has been highlighted across docking, ADME/Tox profiling, and molecular dynamics studies for maximizing binding interactions while assuring safety and optimal pharmacokinetics for targeting the intestinal-localized α-glucosidase enzyme. Overall, this study provided valuable starting points for developing new α-glucosidase inhibitors based on nature-derived unique scaffolds, as well as guidance for prospective lead optimization and development within future pre-clinical and clinical investigations.
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Affiliation(s)
- Sameh S. Elhady
- Department of Natural Products, Faculty of Pharmacy, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (N.M.A.); (M.A.E.); (A.E.K.)
| | - Noha M. Alshobaki
- Department of Natural Products, Faculty of Pharmacy, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (N.M.A.); (M.A.E.); (A.E.K.)
| | - Mahmoud A. Elfaky
- Department of Natural Products, Faculty of Pharmacy, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (N.M.A.); (M.A.E.); (A.E.K.)
- Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Abdulrahman E. Koshak
- Department of Natural Products, Faculty of Pharmacy, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (N.M.A.); (M.A.E.); (A.E.K.)
| | - Majed Alharbi
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
| | - Reda F. A. Abdelhameed
- Department of Pharmacognosy, Faculty of Pharmacy, Galala University, New Galala 43713, Egypt;
- Department of Pharmacognosy, Faculty of Pharmacy, Suez Canal University, Ismailia 41522, Egypt
| | - Khaled M. Darwish
- Department of Medicinal Chemistry, Faculty of Pharmacy, Suez Canal University, Ismailia 41522, Egypt
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16
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Costa L, Sousa E, Fernandes C. Cyclic Peptides in Pipeline: What Future for These Great Molecules? Pharmaceuticals (Basel) 2023; 16:996. [PMID: 37513908 PMCID: PMC10386233 DOI: 10.3390/ph16070996] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 07/06/2023] [Accepted: 07/08/2023] [Indexed: 07/30/2023] Open
Abstract
Cyclic peptides are molecules that are already used as drugs in therapies approved for various pharmacological activities, for example, as antibiotics, antifungals, anticancer, and immunosuppressants. Interest in these molecules has been growing due to the improved pharmacokinetic and pharmacodynamic properties of the cyclic structure over linear peptides and by the evolution of chemical synthesis, computational, and in vitro methods. To date, 53 cyclic peptides have been approved by different regulatory authorities, and many others are in clinical trials for a wide diversity of conditions. In this review, the potential of cyclic peptides is presented, and general aspects of their synthesis and development are discussed. Furthermore, an overview of already approved cyclic peptides is also given, and the cyclic peptides in clinical trials are summarized.
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Affiliation(s)
- Lia Costa
- Laboratório de Química Orgânica e Farmacêutica, Departamento de Ciências Químicas, Faculdade de Farmácia, Universidade do Porto, Rua de Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal;
- Interdisciplinary Centre of Marine and Environmental Research (CIIMAR), Edifício do Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos s/n, 4050-208 Matosinhos, Portugal
| | - Emília Sousa
- Laboratório de Química Orgânica e Farmacêutica, Departamento de Ciências Químicas, Faculdade de Farmácia, Universidade do Porto, Rua de Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal;
- Interdisciplinary Centre of Marine and Environmental Research (CIIMAR), Edifício do Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos s/n, 4050-208 Matosinhos, Portugal
| | - Carla Fernandes
- Laboratório de Química Orgânica e Farmacêutica, Departamento de Ciências Químicas, Faculdade de Farmácia, Universidade do Porto, Rua de Jorge Viterbo Ferreira, 228, 4050-313 Porto, Portugal;
- Interdisciplinary Centre of Marine and Environmental Research (CIIMAR), Edifício do Terminal de Cruzeiros do Porto de Leixões, Av. General Norton de Matos s/n, 4050-208 Matosinhos, Portugal
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17
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Chew PY, Joseph JA, Collepardo-Guevara R, Reinhardt A. Thermodynamic origins of two-component multiphase condensates of proteins. Chem Sci 2023; 14:1820-1836. [PMID: 36819870 PMCID: PMC9931050 DOI: 10.1039/d2sc05873a] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 01/06/2023] [Indexed: 01/26/2023] Open
Abstract
Intracellular condensates are highly multi-component systems in which complex phase behaviour can ensue, including the formation of architectures comprising multiple immiscible condensed phases. Relying solely on physical intuition to manipulate such condensates is difficult because of the complexity of their composition, and systematically learning the underlying rules experimentally would be extremely costly. We address this challenge by developing a computational approach to design pairs of protein sequences that result in well-separated multilayered condensates and elucidate the molecular origins of these compartments. Our method couples a genetic algorithm to a residue-resolution coarse-grained protein model. We demonstrate that we can design protein partners to form multiphase condensates containing naturally occurring proteins, such as the low-complexity domain of hnRNPA1 and its mutants, and show how homo- and heterotypic interactions must differ between proteins to result in multiphasicity. We also show that in some cases the specific pattern of amino-acid residues plays an important role. Our findings have wide-ranging implications for understanding and controlling the organisation, functions and material properties of biomolecular condensates.
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Affiliation(s)
- Pin Yu Chew
- Yusuf Hamied Department of Chemistry, University of Cambridge Cambridge CB2 1EW UK
| | - Jerelle A. Joseph
- Yusuf Hamied Department of Chemistry, University of CambridgeCambridgeCB2 1EWUK,Department of Physics, University of CambridgeCambridgeCB3 0HEUK,Department of Genetics, University of CambridgeCambridgeCB2 3EHUK
| | - Rosana Collepardo-Guevara
- Yusuf Hamied Department of Chemistry, University of Cambridge Cambridge CB2 1EW UK .,Department of Physics, University of Cambridge Cambridge CB3 0HE UK.,Department of Genetics, University of Cambridge Cambridge CB2 3EH UK
| | - Aleks Reinhardt
- Yusuf Hamied Department of Chemistry, University of Cambridge Cambridge CB2 1EW UK
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18
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Ivanenkov YA, Polykovskiy D, Bezrukov D, Zagribelnyy B, Aladinskiy V, Kamya P, Aliper A, Ren F, Zhavoronkov A. Chemistry42: An AI-Driven Platform for Molecular Design and Optimization. J Chem Inf Model 2023; 63:695-701. [PMID: 36728505 PMCID: PMC9930109 DOI: 10.1021/acs.jcim.2c01191] [Citation(s) in RCA: 29] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Chemistry42 is a software platform for de novo small molecule design and optimization that integrates Artificial Intelligence (AI) techniques with computational and medicinal chemistry methodologies. Chemistry42 efficiently generates novel molecular structures with optimized properties validated in both in vitro and in vivo studies and is available through licensing or collaboration. Chemistry42 is the core component of Insilico Medicine's Pharma.ai drug discovery suite. Pharma.ai also includes PandaOmics for target discovery and multiomics data analysis, and inClinico─a data-driven multimodal forecast of a clinical trial's probability of success (PoS). In this paper, we demonstrate how the platform can be used to efficiently find novel molecular structures against DDR1 and CDK20.
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Affiliation(s)
- Yan A. Ivanenkov
- Insilico
Medicine Kong Kong Ltd., Unit 310, 3/F, Building 8W, Phase 2, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Daniil Polykovskiy
- Insilico
Medicine Canada Inc., 3710-1250 René-Lévesque Blvd W, Montreal, Quebec, H3B
4W8 Canada
| | - Dmitry Bezrukov
- Insilico
Medicine Kong Kong Ltd., Unit 310, 3/F, Building 8W, Phase 2, Hong Kong Science Park, Pak Shek Kok, Hong Kong
| | - Bogdan Zagribelnyy
- Insilico
Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, Masdar City, PO Box 145748, Abu Dhabi, UAE
| | - Vladimir Aladinskiy
- Insilico
Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, Masdar City, PO Box 145748, Abu Dhabi, UAE
| | - Petrina Kamya
- Insilico
Medicine Canada Inc., 3710-1250 René-Lévesque Blvd W, Montreal, Quebec, H3B
4W8 Canada
| | - Alex Aliper
- Insilico
Medicine AI Limited, Level 6, Unit 08, Block A, IRENA HQ Building, Masdar City, PO Box 145748, Abu Dhabi, UAE
| | - Feng Ren
- Insilico
Medicine Shanghai Ltd., Suite 901, Tower C, Changtai Plaza, 2889 Jinke Road, Pudong New District, Shanghai 201203, China
| | - Alex Zhavoronkov
- Insilico
Medicine Kong Kong Ltd., Unit 310, 3/F, Building 8W, Phase 2, Hong Kong Science Park, Pak Shek Kok, Hong Kong,
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19
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Iraji MS, Tanha J, Habibinejad M. Druggable protein prediction using a multi-canal deep convolutional neural network based on autocovariance method. Comput Biol Med 2022; 151:106276. [PMID: 36410099 DOI: 10.1016/j.compbiomed.2022.106276] [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: 07/05/2022] [Revised: 10/18/2022] [Accepted: 10/30/2022] [Indexed: 11/09/2022]
Abstract
Drug targets must be identified and positioned correctly to research and manufacture new drugs. In this study, rather than using traditional methods for drug expansion, the drug target is determined using machine learning. Machine learning has generated significant interest and desire in recent years and extensive research due to its low cost and speed of operation. As a result, it is critical to develop an intelligent classification system for drug proteins. This study proposes two distinct models for the prediction of druggable protein classes based on the deep learning method. The translation of drug-protein sequences is based on six physicochemical properties of amino acids. Following the application of the autocovariance method, converted sequences are used as fixed-length input vectors in deep stacked sparse auto-encoders (DSSAEs) network. The coded protein sequences are also considered and utilized as a six-channel input vector for the deep convolutional neural network model. The experimental results contributing to the deep convolution model are more efficient than previous studies for classifying druggable proteins. The proposed approach achieved a sensitivity of 96.92%, a specificity of 99.51%, and an accuracy of 98.29%.
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Affiliation(s)
- Mohammad Saber Iraji
- Department of Computer Engineering and Information Technology, Payame Noor University, Tehran, Iran; Department of Computer Engineering, University of Tabriz, Tabriz, Iran.
| | - Jafar Tanha
- Department of Computer Engineering, University of Tabriz, Tabriz, Iran
| | - Mahboobeh Habibinejad
- Department of Computer Engineering and Information Technology, Payame Noor University, Tehran, Iran
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20
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Hernández-Silva D, Alcaraz-Pérez F, Pérez-Sánchez H, Cayuela ML. Virtual screening and zebrafish models in tandem, for drug discovery and development. Expert Opin Drug Discov 2022:1-13. [DOI: 10.1080/17460441.2022.2147503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Affiliation(s)
- David Hernández-Silva
- Telomerase, Cancer and Aging Group (TCAG), Hospital Clínico Universitario Virgen de la Arrixaca, 30120 Murcia, Spain
- Instituto Murciano de Investigación Biosanitaria-Arrixaca (IMIB-Arrixaca), 30120 Murcia, Spain
- Structural Bioinformatics and High-Performance Computing Research Group (BIOHPC), Computer Engineering Department, Universidad Católica de Murcia (UCAM), Guadalupe, 30107 Murcia, Spain
| | - Francisca Alcaraz-Pérez
- Telomerase, Cancer and Aging Group (TCAG), Hospital Clínico Universitario Virgen de la Arrixaca, 30120 Murcia, Spain
- Instituto Murciano de Investigación Biosanitaria-Arrixaca (IMIB-Arrixaca), 30120 Murcia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), ISCIII, 30100 Murcia, Spain
| | - Horacio Pérez-Sánchez
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), ISCIII, 30100 Murcia, Spain
| | - Maria Luisa Cayuela
- Telomerase, Cancer and Aging Group (TCAG), Hospital Clínico Universitario Virgen de la Arrixaca, 30120 Murcia, Spain
- Instituto Murciano de Investigación Biosanitaria-Arrixaca (IMIB-Arrixaca), 30120 Murcia, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), ISCIII, 30100 Murcia, Spain
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21
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Islam MA, Barshetty MM, Srinivasan S, Dudekula DB, Rallabandi VPS, Mohammed S, Natarajan S, Park J. Identification of Novel Ribonucleotide Reductase Inhibitors for Therapeutic Application in Bile Tract Cancer: An Advanced Pharmacoinformatics Study. Biomolecules 2022; 12:biom12091279. [PMID: 36139117 PMCID: PMC9496582 DOI: 10.3390/biom12091279] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/05/2022] [Accepted: 09/08/2022] [Indexed: 11/28/2022] Open
Abstract
Biliary tract cancer (BTC) is constituted by a heterogeneous group of malignant tumors that may develop in the biliary tract, and it is the second most common liver cancer. Human ribonucleotide reductase M1 (hRRM1) has already been proven to be a potential BTC target. In the current study, a de novo design approach was used to generate novel and effective chemical therapeutics for BTC. A set of comprehensive pharmacoinformatics approaches was implemented and, finally, seventeen potential molecules were found to be effective for the modulation of hRRM1 activity. Molecular docking, negative image-based ShaEP scoring, absolute binding free energy, in silico pharmacokinetics, and toxicity assessments corroborated the potentiality of the selected molecules. Almost all molecules showed higher affinity in comparison to gemcitabine and naphthyl salicylic acyl hydrazone (NSAH). On binding interaction analysis, a number of critical amino acids was found to hold the molecules at the active site cavity. The molecular dynamics (MD) simulation study also indicated the stability between protein and ligands. High negative MM-GBSA (molecular mechanics generalized Born and surface area) binding free energy indicated the potentiality of the molecules. Therefore, the proposed molecules might have the potential to be effective therapeutics for the management of BTC.
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Affiliation(s)
- Md Ataul Islam
- 3BIGS Omicscore Private Limited, 909 Lavelle Building, Richmond Circle, Bangalore 560025, India
| | | | - Sridhar Srinivasan
- 3BIGS Omicscore Private Limited, 909 Lavelle Building, Richmond Circle, Bangalore 560025, India
| | - Dawood Babu Dudekula
- 3BIGS Omicscore Private Limited, 909 Lavelle Building, Richmond Circle, Bangalore 560025, India
| | | | - Sameer Mohammed
- 3BIGS Omicscore Private Limited, 909 Lavelle Building, Richmond Circle, Bangalore 560025, India
| | | | - Junhyung Park
- 3BIGS Co., Ltd., B-831, Geumgang Penterium IX Tower, Hwaseong 18469, Korea
- Correspondence:
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22
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Liu Z, Du J, Lin Z, Li Z, Liu B, Cui Z, Fang J, Xie L. DenovoProfiling: A webserver for de novo generated molecule library profiling. Comput Struct Biotechnol J 2022; 20:4082-4097. [PMID: 36016718 PMCID: PMC9379519 DOI: 10.1016/j.csbj.2022.07.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 07/25/2022] [Accepted: 07/25/2022] [Indexed: 01/10/2023] Open
Abstract
Various deep learning-based architectures for molecular generation have been proposed for de novo drug design. The flourish of the de novo molecular generation methods and applications has created a great demand for the visualization and functional profiling for the de novo generated molecules. An increasing number of publicly available chemogenomic databases sets good foundations and creates good opportunities for comprehensive profiling of the de novo library. In this paper, we present DenovoProfiling, a webserver dedicated to de novo library visualization and functional profiling. Currently, DenovoProfiling contains six modules: (1) identification & visualization module for chemical structure visualization and identify the reported structures, (2) chemical space module for chemical space exploration using similarity maps, principal components analysis (PCA), drug-like properties distribution, and scaffold-based clustering, (3) ADMET prediction module for predicting the ADMET properties of the de novo molecules, (4) molecular alignment module for three dimensional molecular shape analysis, (5) drugs mapping module for identifying structural similar drugs, and (6) target & pathway module for identifying the reported targets and corresponding functional pathways. DenovoProfiling could provide structural identification, chemical space exploration, drug mapping, and target & pathway information. The comprehensive annotated information could give users a clear picture of their de novo library and could guide the further selection of candidates for chemical synthesis and biological confirmation. DenovoProfiling is freely available at http://denovoprofiling.xielab.net.
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Key Words
- DDR1, Discovered potent discoidin domain receptor 1
- De novo drug design
- De novo molecule library
- Deep learning
- FBDD, Fragment-based drug design
- FDR, False discovery rate
- GAN, Generative adversarial networks
- HTS, High throughput screening
- LSTM, Long short-term memory
- Library profiling
- PCA, Principal components analysis
- RNN, Recurrent neural networks
- SCA, Scaffold-based classification approach
- VAE, Variational autoencoders
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Affiliation(s)
- Zhihong Liu
- School of Public Health, Xinxiang Medical University, Xinxiang, China
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China
| | - Jiewen Du
- Beijing Jingpai Technology Co., Ltd., 1500-1, Hailong Building Z-Park, Beijing 100090, China
| | - Ziying Lin
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China
| | - Ze Li
- School of Public Health, Xinxiang Medical University, Xinxiang, China
| | - Bingdong Liu
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China
| | - Zongbin Cui
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China
| | - Jiansong Fang
- Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China
- Corresponding authors at: School of Public Health, Xinxiang Medical University, Xinxiang, China (L. Xie). Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China (J. Fang).
| | - Liwei Xie
- School of Public Health, Xinxiang Medical University, Xinxiang, China
- Guangdong Provincial Key Laboratory of Microbial Culture Collection and Application, State Key Laboratory of Applied Microbiology Southern China, Institute of Microbiology, Guangdong Academy of Sciences, Guangzhou 510070, China
- Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Corresponding authors at: School of Public Health, Xinxiang Medical University, Xinxiang, China (L. Xie). Science and Technology Innovation Center, Guangzhou University of Chinese Medicine, Guangzhou, China (J. Fang).
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23
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Kulczyk S, Koszytkowska-Stawińska M. Novel drug design framework as a response to neglected and emerging diseases. J Biomol Struct Dyn 2022:1-12. [DOI: 10.1080/07391102.2022.2110519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Affiliation(s)
- Stanisław Kulczyk
- Faculty of Chemistry, Warsaw University of Technology, Warsaw, Poland
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24
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Menon D, Ranganathan R. A Generative Approach to Materials Discovery, Design, and Optimization. ACS OMEGA 2022; 7:25958-25973. [PMID: 35936396 PMCID: PMC9352221 DOI: 10.1021/acsomega.2c03264] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 07/11/2022] [Indexed: 05/25/2023]
Abstract
Despite its potential to transform society, materials research suffers from a major drawback: its long research timeline. Recently, machine-learning techniques have emerged as a viable solution to this drawback and have shown accuracies comparable to other computational techniques like density functional theory (DFT) at a fraction of the computational time. One particular class of machine-learning models, known as "generative models", is of particular interest owing to its ability to approximate high-dimensional probability distribution functions, which in turn can be used to generate novel data such as molecular structures by sampling these approximated probability distribution functions. This review article aims to provide an in-depth understanding of the underlying mathematical principles of popular generative models such as recurrent neural networks, variational autoencoders, and generative adversarial networks and discuss their state-of-the-art applications in the domains of biomaterials and organic drug-like materials, energy materials, and structural materials. Here, we discuss a broad range of applications of these models spanning from the discovery of drugs that treat cancer to finding the first room-temperature superconductor and from the discovery and optimization of battery and photovoltaic materials to the optimization of high-entropy alloys. We conclude by presenting a brief outlook of the major challenges that lie ahead for the mainstream usage of these models for materials research.
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25
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Wang J, Chu Y, Mao J, Jeon HN, Jin H, Zeb A, Jang Y, Cho KH, Song T, No KT. De novo molecular design with deep molecular generative models for PPI inhibitors. Brief Bioinform 2022; 23:6643455. [PMID: 35830870 DOI: 10.1093/bib/bbac285] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 06/14/2022] [Accepted: 06/20/2022] [Indexed: 12/27/2022] Open
Abstract
We construct a protein-protein interaction (PPI) targeted drug-likeness dataset and propose a deep molecular generative framework to generate novel drug-likeness molecules from the features of the seed compounds. This framework gains inspiration from published molecular generative models, uses the key features associated with PPI inhibitors as input and develops deep molecular generative models for de novo molecular design of PPI inhibitors. For the first time, quantitative estimation index for compounds targeting PPI was applied to the evaluation of the molecular generation model for de novo design of PPI-targeted compounds. Our results estimated that the generated molecules had better PPI-targeted drug-likeness and drug-likeness. Additionally, our model also exhibits comparable performance to other several state-of-the-art molecule generation models. The generated molecules share chemical space with iPPI-DB inhibitors as demonstrated by chemical space analysis. The peptide characterization-oriented design of PPI inhibitors and the ligand-based design of PPI inhibitors are explored. Finally, we recommend that this framework will be an important step forward for the de novo design of PPI-targeted therapeutics.
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Affiliation(s)
- Jianmin Wang
- The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon 21983, Republic of Korea.,Bioinformatics and Molecular Design Research Center (BMDRC), Incheon 21983, Republic of Korea
| | - Yanyi Chu
- State Key Laboratory of Microbial Metabolism, Shanghai-Islamabad Belgrade Joint Innovation Center on Antibacterial Resistances, Joint International Research Laboratory of Metabolic & Developmental Sciences and School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200030, P.R. China
| | - Jiashun Mao
- The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon 21983, Republic of Korea.,Bioinformatics and Molecular Design Research Center (BMDRC), Incheon 21983, Republic of Korea
| | - Hyeon-Nae Jeon
- Bioinformatics and Molecular Design Research Center (BMDRC), Incheon 21983, Republic of Korea.,Biotechnology, College of Life Science and Biotechnology, Yonsei University, Seoul 03722, Republic of Korea
| | - Haiyan Jin
- The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon 21983, Republic of Korea.,Bioinformatics and Molecular Design Research Center (BMDRC), Incheon 21983, Republic of Korea
| | - Amir Zeb
- The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon 21983, Republic of Korea.,Department of Natural and Basic Sciences, University of Turbat, 92600, Pakistan
| | - Yuil Jang
- The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon 21983, Republic of Korea.,Bioinformatics and Molecular Design Research Center (BMDRC), Incheon 21983, Republic of Korea
| | - Kwang-Hwi Cho
- School of Systems Biomedical Science, Soongsil University, Seoul, Republic of Korea
| | - Tao Song
- School of Computer Science and Technology, China University of Petroleum, Qingdao, 266580, Shandong, China
| | - Kyoung Tai No
- The Interdisciplinary Graduate Program in Integrative Biotechnology and Translational Medicine, Yonsei University, Incheon 21983, Republic of Korea.,Bioinformatics and Molecular Design Research Center (BMDRC), Incheon 21983, Republic of Korea
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26
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Elend L, Jacobsen L, Cofala T, Prellberg J, Teusch T, Kramer O, Solov’yov IA. Design of SARS-CoV-2 Main Protease Inhibitors Using Artificial Intelligence and Molecular Dynamic Simulations. Molecules 2022; 27:molecules27134020. [PMID: 35807268 PMCID: PMC9268208 DOI: 10.3390/molecules27134020] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 06/16/2022] [Accepted: 06/20/2022] [Indexed: 12/10/2022] Open
Abstract
Drug design is a time-consuming and cumbersome process due to the vast search space of drug-like molecules and the difficulty of investigating atomic and electronic interactions. The present paper proposes a computational drug design workflow that combines artificial intelligence (AI) methods, i.e., an evolutionary algorithm and artificial neural network model, and molecular dynamics (MD) simulations to design and evaluate potential drug candidates. For the purpose of illustration, the proposed workflow was applied to design drug candidates against the main protease of severe acute respiratory syndrome coronavirus 2. From the ∼140,000 molecules designed using AI methods, MD analysis identified two molecules as potential drug candidates.
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Affiliation(s)
- Lars Elend
- Computational Intelligence Lab, Department of Computer Science, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany; (L.E.); (T.C.); (J.P.)
| | - Luise Jacobsen
- Department of Physics, Chemistry and Pharmacy, University of Southern Denmark, Campusvej 55, 5230 Odense M, Denmark;
| | - Tim Cofala
- Computational Intelligence Lab, Department of Computer Science, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany; (L.E.); (T.C.); (J.P.)
| | - Jonas Prellberg
- Computational Intelligence Lab, Department of Computer Science, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany; (L.E.); (T.C.); (J.P.)
| | - Thomas Teusch
- Department of Physics, Carl von Ossietzky University, Carl-von-Ossietzky-Str. 9-11, 26129 Oldenburg, Germany;
| | - Oliver Kramer
- Computational Intelligence Lab, Department of Computer Science, Carl von Ossietzky University, Ammerländer Heerstraße 114-118, 26129 Oldenburg, Germany; (L.E.); (T.C.); (J.P.)
- Correspondence: (O.K.); (I.A.S.); Tel.: +49-441-798-3817 (I.A.S.)
| | - Ilia A. Solov’yov
- Department of Physics, Carl von Ossietzky University, Carl-von-Ossietzky-Str. 9-11, 26129 Oldenburg, Germany;
- Research Center for Neurosensory Science, Carl von Ossietzky Universität Oldenburg, 26111 Oldenburg, Germany
- Center for Nanoscale Dynamics (CENAD), Carl von Ossietzky Universität Oldenburg, Institut für Physik, Ammerländer Heerstr. 114-118, 26129 Oldenburg, Germany
- Correspondence: (O.K.); (I.A.S.); Tel.: +49-441-798-3817 (I.A.S.)
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27
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An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning. Molecules 2022; 27:molecules27103112. [PMID: 35630587 PMCID: PMC9147181 DOI: 10.3390/molecules27103112] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/05/2022] [Accepted: 05/10/2022] [Indexed: 11/19/2022] Open
Abstract
In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.
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28
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Bai Q, Liu S, Tian Y, Xu T, Banegas‐Luna AJ, Pérez‐Sánchez H, Huang J, Liu H, Yao X. Application advances of deep learning methods for de novo drug design and molecular dynamics simulation. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1581] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Qifeng Bai
- Key Lab of Preclinical Study for New Drugs of Gansu Province Institute of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Lanzhou University Lanzhou Gansu China
| | - Shuo Liu
- School of Pharmacy Lanzhou University Lanzhou Gansu China
| | - Yanan Tian
- School of Pharmacy Lanzhou University Lanzhou Gansu China
| | - Tingyang Xu
- Tencent AI Lab, Shenzhen Tencent Computer Ltd Shenzhen China
| | - Antonio Jesús Banegas‐Luna
- Structural Bioinformatics and High Performance Computing Research Group (BIO‐HPC), Computer Engineering Department UCAM Universidad Católica de Murcia Murcia Spain
| | - Horacio Pérez‐Sánchez
- Structural Bioinformatics and High Performance Computing Research Group (BIO‐HPC), Computer Engineering Department UCAM Universidad Católica de Murcia Murcia Spain
| | - Junzhou Huang
- Tencent AI Lab, Shenzhen Tencent Computer Ltd Shenzhen China
| | - Huanxiang Liu
- School of Pharmacy Lanzhou University Lanzhou Gansu China
| | - Xiaojun Yao
- College of Chemistry and Chemical Engineering Lanzhou University Lanzhou Gansu China
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29
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Nigam A, Pollice R, Aspuru-Guzik A. Parallel tempered genetic algorithm guided by deep neural networks for inverse molecular design. DIGITAL DISCOVERY 2022; 1:390-404. [PMID: 36091415 PMCID: PMC9358752 DOI: 10.1039/d2dd00003b] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Accepted: 05/03/2022] [Indexed: 12/30/2022]
Abstract
We present JANUS, an evolutionary algorithm for inverse molecular design. It propagates an explorative and an exploitative population exchanging members via parallel tempering and uses active learning via deep neural networks to enhance sampling.
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Affiliation(s)
- AkshatKumar Nigam
- Department of Computer Science, Stanford University, USA
- Department of Computer Science, University of Toronto, Canada
- Department of Chemistry, University of Toronto, Canada
| | - Robert Pollice
- Department of Computer Science, University of Toronto, Canada
- Department of Chemistry, University of Toronto, Canada
| | - Alán Aspuru-Guzik
- Department of Computer Science, University of Toronto, Canada
- Department of Chemistry, University of Toronto, Canada
- Vector Institute for Artificial Intelligence, Toronto, Canada
- Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR), 661 University Ave, Toronto, Ontario M5G, Canada
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30
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Chávez-Hernández AL, Juárez-Mercado KE, Saldívar-González FI, Medina-Franco JL. Towards the De Novo Design of HIV-1 Protease Inhibitors Based on Natural Products. Biomolecules 2021; 11:1805. [PMID: 34944448 PMCID: PMC8698858 DOI: 10.3390/biom11121805] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 11/22/2021] [Accepted: 11/29/2021] [Indexed: 01/14/2023] Open
Abstract
Acquired immunodeficiency syndrome (AIDS) caused by the human immunodeficiency virus (HIV) continues to be a public health problem. In 2020, 680,000 people died from HIV-related causes, and 1.5 million people were infected. Antiretrovirals are a way to control HIV infection but not to cure AIDS. As such, effective treatment must be developed to control AIDS. Developing a drug is not an easy task, and there is an enormous amount of work and economic resources invested. For this reason, it is highly convenient to employ computer-aided drug design methods, which can help generate and identify novel molecules. Using the de novo design, novel molecules can be developed using fragments as building blocks. In this work, we develop a virtual focused compound library of HIV-1 viral protease inhibitors from natural product fragments. Natural products are characterized by a large diversity of functional groups, many sp3 atoms, and chiral centers. Pseudo-natural products are a combination of natural products fragments that keep the desired structural characteristics from different natural products. An interactive version of chemical space visualization of virtual compounds focused on HIV-1 viral protease inhibitors from natural product fragments is freely available in the supplementary material.
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Affiliation(s)
| | | | | | - José L. Medina-Franco
- DIFACQUIM Research Group, Department of Pharmacy, School of Chemistry, Universidad Nacional Autónoma de México, Avenida Universidad 3000, Mexico City 04510, Mexico; (A.L.C.-H.); (K.E.J.-M.); (F.I.S.-G.)
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31
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Pereira T, Abbasi M, Oliveira JL, Ribeiro B, Arrais J. Optimizing blood-brain barrier permeation through deep reinforcement learning for de novo drug design. Bioinformatics 2021; 37:i84-i92. [PMID: 34252946 PMCID: PMC8336597 DOI: 10.1093/bioinformatics/btab301] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022] Open
Abstract
MOTIVATION The process of placing new drugs into the market is time-consuming, expensive and complex. The application of computational methods for designing molecules with bespoke properties can contribute to saving resources throughout this process. However, the fundamental properties to be optimized are often not considered or conflicting with each other. In this work, we propose a novel approach to consider both the biological property and the bioavailability of compounds through a deep reinforcement learning framework for the targeted generation of compounds. We aim to obtain a promising set of selective compounds for the adenosine A2A receptor and, simultaneously, that have the necessary properties in terms of solubility and permeability across the blood-brain barrier to reach the site of action. The cornerstone of the framework is based on a recurrent neural network architecture, the Generator. It seeks to learn the building rules of valid molecules to sample new compounds further. Also, two Predictors are trained to estimate the properties of interest of the new molecules. Finally, the fine-tuning of the Generator was performed with reinforcement learning, integrated with multi-objective optimization and exploratory techniques to ensure that the Generator is adequately biased. RESULTS The biased Generator can generate an interesting set of molecules, with approximately 85% having the two fundamental properties biased as desired. Thus, this approach has transformed a general molecule generator into a model focused on optimizing specific objectives. Furthermore, the molecules' synthesizability and drug-likeness demonstrate the potential applicability of the de novo drug design in medicinal chemistry. AVAILABILITY AND IMPLEMENTATION All code is publicly available in the https://github.com/larngroup/De-Novo-Drug-Design. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Tiago Pereira
- CSUC/DEI, University of Coimbra, Coimbra 3030-290, Portugal.,IEETA/DETI, University of Aveiro, Aveiro 3810-193, Portugal
| | - Maryam Abbasi
- CSUC/DEI, University of Coimbra, Coimbra 3030-290, Portugal
| | | | | | - Joel Arrais
- CSUC/DEI, University of Coimbra, Coimbra 3030-290, Portugal
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33
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Piroozmand F, Mohammadipanah F, Sajedi H. Spectrum of deep learning algorithms in drug discovery. Chem Biol Drug Des 2021; 96:886-901. [PMID: 33058458 DOI: 10.1111/cbdd.13674] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 02/11/2020] [Accepted: 02/19/2020] [Indexed: 12/16/2022]
Abstract
Deep learning (DL) algorithms are a subset of machine learning algorithms with the aim of modeling complex mapping between a set of elements and their classes. In parallel to the advance in revealing the molecular bases of diseases, a notable innovation has been undertaken to apply DL in data/libraries management, reaction optimizations, differentiating uncertainties, molecule constructions, creating metrics from qualitative results, and prediction of structures or interactions. From source identification to lead discovery and medicinal chemistry of the drug candidate, drug delivery, and modification, the challenges can be subjected to artificial intelligence algorithms to aid in the generation and interpretation of data. Discovery and design approach, both demand automation, large data management and data fusion by the advance in high-throughput mode. The application of DL can accelerate the exploration of drug mechanisms, finding novel indications for existing drugs (drug repositioning), drug development, and preclinical and clinical studies. The impact of DL in the workflow of drug discovery, design, and their complementary tools are highlighted in this review. Additionally, the type of DL algorithms used for this purpose, and their pros and cons along with the dominant directions of future research are presented.
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Affiliation(s)
- Firoozeh Piroozmand
- Pharmaceutical Biotechnology Lab, Department of Microbiology, School of Biology and Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran, Tehran, Iran
| | - Fatemeh Mohammadipanah
- Pharmaceutical Biotechnology Lab, Department of Microbiology, School of Biology and Center of Excellence in Phylogeny of Living Organisms, College of Science, University of Tehran, Tehran, Iran
| | - Hedieh Sajedi
- Department of Computer Science, School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
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34
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Steinmann C, Jensen JH. Using a genetic algorithm to find molecules with good docking scores. PEERJ PHYSICAL CHEMISTRY 2021. [DOI: 10.7717/peerj-pchem.18] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
A graph-based genetic algorithm (GA) is used to identify molecules (ligands) with high absolute docking scores as estimated by the Glide software package, starting from randomly chosen molecules from the ZINC database, for four different targets: Bacillus subtilis chorismate mutase (CM), human β2-adrenergic G protein-coupled receptor (β2AR), the DDR1 kinase domain (DDR1), and β-cyclodextrin (BCD). By the combined use of functional group filters and a score modifier based on a heuristic synthetic accessibility (SA) score our approach identifies between ca 500 and 6,000 structurally diverse molecules with scores better than known binders by screening a total of 400,000 molecules starting from 8,000 randomly selected molecules from the ZINC database. Screening 250,000 molecules from the ZINC database identifies significantly more molecules with better docking scores than known binders, with the exception of CM, where the conventional screening approach only identifies 60 compounds compared to 511 with GA+Filter+SA. In the case of β2AR and DDR1, the GA+Filter+SA approach finds significantly more molecules with docking scores lower than −9.0 and −10.0. The GA+Filters+SA docking methodology is thus effective in generating a large and diverse set of synthetically accessible molecules with very good docking scores for a particular target. An early incarnation of the GA+Filter+SA approach was used to identify potential binders to the COVID-19 main protease and submitted to the early stages of the COVID Moonshot project, a crowd-sourced initiative to accelerate the development of a COVID antiviral.
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Affiliation(s)
- Casper Steinmann
- Department of Chemistry and Bioscience, Aalborg University, Aalborg, Denmark
| | - Jan H. Jensen
- Department of Chemistry, University of Copenhagen, Copenhagen, Denmark
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Kunkel C, Margraf JT, Chen K, Oberhofer H, Reuter K. Active discovery of organic semiconductors. Nat Commun 2021; 12:2422. [PMID: 33893287 PMCID: PMC8065160 DOI: 10.1038/s41467-021-22611-4] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Accepted: 03/15/2021] [Indexed: 01/16/2023] Open
Abstract
The versatility of organic molecules generates a rich design space for organic semiconductors (OSCs) considered for electronics applications. Offering unparalleled promise for materials discovery, the vastness of this design space also dictates efficient search strategies. Here, we present an active machine learning (AML) approach that explores an unlimited search space through consecutive application of molecular morphing operations. Evaluating the suitability of OSC candidates on the basis of charge injection and mobility descriptors, the approach successively queries predictive-quality first-principles calculations to build a refining surrogate model. The AML approach is optimized in a truncated test space, providing deep methodological insight by visualizing it as a chemical space network. Significantly outperforming a conventional computational funnel, the optimized AML approach rapidly identifies well-known and hitherto unknown molecular OSC candidates with superior charge conduction properties. Most importantly, it constantly finds further candidates with highest efficiency while continuing its exploration of the endless design space.
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Affiliation(s)
- Christian Kunkel
- Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Garching, Germany
| | - Johannes T Margraf
- Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Garching, Germany
| | - Ke Chen
- Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Garching, Germany
| | - Harald Oberhofer
- Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Garching, Germany
| | - Karsten Reuter
- Chair for Theoretical Chemistry and Catalysis Research Center, Technische Universität München, Garching, Germany.
- Fritz-Haber-Institut der Max-Planck-Gesellschaft, Berlin, Germany.
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36
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Nigam A, Pollice R, Krenn M, Gomes GDP, Aspuru-Guzik A. Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES. Chem Sci 2021; 12:7079-7090. [PMID: 34123336 PMCID: PMC8153210 DOI: 10.1039/d1sc00231g] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 04/12/2021] [Indexed: 11/23/2022] Open
Abstract
Inverse design allows the generation of molecules with desirable physical quantities using property optimization. Deep generative models have recently been applied to tackle inverse design, as they possess the ability to optimize molecular properties directly through structure modification using gradients. While the ability to carry out direct property optimizations is promising, the use of generative deep learning models to solve practical problems requires large amounts of data and is very time-consuming. In this work, we propose STONED - a simple and efficient algorithm to perform interpolation and exploration in the chemical space, comparable to deep generative models. STONED bypasses the need for large amounts of data and training times by using string modifications in the SELFIES molecular representation. First, we achieve non-trivial performance on typical benchmarks for generative models without any training. Additionally, we demonstrate applications in high-throughput virtual screening for the design of drugs, photovoltaics, and the construction of chemical paths, allowing for both property and structure-based interpolation in the chemical space. Overall, we anticipate our results to be a stepping stone for developing more sophisticated inverse design models and benchmarking tools, ultimately helping generative models achieve wider adoption.
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Affiliation(s)
- AkshatKumar Nigam
- Department of Computer Science, University of Toronto Canada
- Department of Chemistry, University of Toronto Canada
| | - Robert Pollice
- Department of Computer Science, University of Toronto Canada
- Department of Chemistry, University of Toronto Canada
| | - Mario Krenn
- Department of Computer Science, University of Toronto Canada
- Department of Chemistry, University of Toronto Canada
- Vector Institute for Artificial Intelligence Toronto Canada
| | - Gabriel Dos Passos Gomes
- Department of Computer Science, University of Toronto Canada
- Department of Chemistry, University of Toronto Canada
| | - Alán Aspuru-Guzik
- Department of Computer Science, University of Toronto Canada
- Department of Chemistry, University of Toronto Canada
- Vector Institute for Artificial Intelligence Toronto Canada
- Lebovic Fellow, Canadian Institute for Advanced Research (CIFAR) 661 University Ave Toronto Ontario M5G Canada
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37
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Kwon Y, Lee J. MolFinder: an evolutionary algorithm for the global optimization of molecular properties and the extensive exploration of chemical space using SMILES. J Cheminform 2021; 13:24. [PMID: 33736687 PMCID: PMC7977239 DOI: 10.1186/s13321-021-00501-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Accepted: 02/27/2021] [Indexed: 12/22/2022] Open
Abstract
Here, we introduce a new molecule optimization method, MolFinder, based on an efficient global optimization algorithm, the conformational space annealing algorithm, and the SMILES representation. MolFinder finds diverse molecules with desired properties efficiently without any training and a large molecular database. Compared with recently proposed reinforcement-learning-based molecule optimization algorithms, MolFinder consistently outperforms in terms of both the optimization of a given target property and the generation of a set of diverse and novel molecules. The efficiency of MolFinder demonstrates that combinatorial optimization using the SMILES representation is a promising approach for molecule optimization, which has not been well investigated despite its simplicity. We believe that our results shed light on new possibilities for advances in molecule optimization methods.
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Affiliation(s)
- Yongbeom Kwon
- Department of Chemistry, Division of Chemistry and Biochemistry, Kangwon National University, 1 Gangwondaehak-gil, Chuncheon, 24341, Republic of Korea.,Arontier Inc., 15F, 241, Gangnam-daero, Seocho-gu, Seoul, 06735, Republic of Korea
| | - Juyong Lee
- Department of Chemistry, Division of Chemistry and Biochemistry, Kangwon National University, 1 Gangwondaehak-gil, Chuncheon, 24341, Republic of Korea.
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38
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Mouchlis VD, Afantitis A, Serra A, Fratello M, Papadiamantis AG, Aidinis V, Lynch I, Greco D, Melagraki G. Advances in de Novo Drug Design: From Conventional to Machine Learning Methods. Int J Mol Sci 2021; 22:1676. [PMID: 33562347 PMCID: PMC7915729 DOI: 10.3390/ijms22041676] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 01/31/2021] [Accepted: 01/31/2021] [Indexed: 12/11/2022] Open
Abstract
. De novo drug design is a computational approach that generates novel molecular structures from atomic building blocks with no a priori relationships. Conventional methods include structure-based and ligand-based design, which depend on the properties of the active site of a biological target or its known active binders, respectively. Artificial intelligence, including machine learning, is an emerging field that has positively impacted the drug discovery process. Deep reinforcement learning is a subdivision of machine learning that combines artificial neural networks with reinforcement-learning architectures. This method has successfully been employed to develop novel de novo drug design approaches using a variety of artificial networks including recurrent neural networks, convolutional neural networks, generative adversarial networks, and autoencoders. This review article summarizes advances in de novo drug design, from conventional growth algorithms to advanced machine-learning methodologies and highlights hot topics for further development.
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Affiliation(s)
| | - Antreas Afantitis
- Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1046, Cyprus;
| | - Angela Serra
- Faculty of Medicine and Health Technology, Tampere University, 33520 Tampere, Finland; (A.S.); (M.F.); (D.G.)
- BioMEdiTech Institute, Tampere University, 33520 Tampere, Finland
| | - Michele Fratello
- Faculty of Medicine and Health Technology, Tampere University, 33520 Tampere, Finland; (A.S.); (M.F.); (D.G.)
- BioMEdiTech Institute, Tampere University, 33520 Tampere, Finland
| | - Anastasios G. Papadiamantis
- Department of ChemoInformatics, NovaMechanics Ltd., Nicosia 1046, Cyprus;
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK;
| | - Vassilis Aidinis
- Institute for Bioinnovation, Biomedical Sciences Research Center Alexander Fleming, Fleming 34, 16672 Athens, Greece;
| | - Iseult Lynch
- School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham B15 2TT, UK;
| | - Dario Greco
- Faculty of Medicine and Health Technology, Tampere University, 33520 Tampere, Finland; (A.S.); (M.F.); (D.G.)
- BioMEdiTech Institute, Tampere University, 33520 Tampere, Finland
- Institute of Biotechnology, University of Helsinki, 00014 Helsinki, Finland
- Finnish Center for Alternative Methods (FICAM), Tampere University, 33520 Tampere, Finland
| | - Georgia Melagraki
- Division of Physical Sciences & Applications, Hellenic Military Academy, 16672 Vari, Greece
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Lambrinidis G, Tsantili-Kakoulidou A. Multi-objective optimization methods in novel drug design. Expert Opin Drug Discov 2020; 16:647-658. [PMID: 33353441 DOI: 10.1080/17460441.2021.1867095] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Introduction: In multi-objective drug design, optimization gains importance, being upgraded to a discipline that attracts its own research. Current strategies are broadly classified into single - objective optimization (SOO) and multi-objective optimization (MOO).Areas covered: Starting with SOO and the ways used to incorporate multiple criteria into it, the present review focuses on MOO techniques, their comparison, advantages, and restrictions. Pareto analysis and the concept of dominance stand in the core of MOO. The Pareto front, Pareto ranking, and limitations of Pareto-based methods, due to high dimensions and data uncertainty, are outlined. Desirability functions and the weighted sum approaches are described as stand-alone techniques to transform the MOO problem to SOO or in combination with pareto analysis and evolutionary algorithms. Representative applications in different drug research areas are also discussed.Expert opinion: Despite their limitations, the use of combined MOO techniques, as well as being complementary to SOO or in conjunction with artificial intelligence, contributes dramatically to efficient drug design, assisting decisions and increasing success probabilities. For multi-target drug design, optimization is supported by network approaches, while applicability of MOO to other fields like drug technology or biological complexity opens new perspectives in the interrelated fields of medicinal chemistry and molecular biology.
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Affiliation(s)
- George Lambrinidis
- Division of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Zografou, Athens, Greece
| | - Anna Tsantili-Kakoulidou
- Division of Pharmaceutical Chemistry, Department of Pharmacy, National and Kapodistrian University of Athens, Panepistimiopolis, Zografou, Athens, Greece
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Multi-objective biofilm algorithm (MOBifi) for de novo drug design with special focus to anti-diabetic drugs. Appl Soft Comput 2020. [DOI: 10.1016/j.asoc.2020.106655] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Leguy J, Cauchy T, Glavatskikh M, Duval B, Da Mota B. EvoMol: a flexible and interpretable evolutionary algorithm for unbiased de novo molecular generation. J Cheminform 2020; 12:55. [PMID: 33431049 PMCID: PMC7494000 DOI: 10.1186/s13321-020-00458-z] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 08/31/2020] [Indexed: 11/24/2022] Open
Abstract
The objective of this work is to design a molecular generator capable of exploring known as well as unfamiliar areas of the chemical space. Our method must be flexible to adapt to very different problems. Therefore, it has to be able to work with or without the influence of prior data and knowledge. Moreover, regardless of the success, it should be as interpretable as possible to allow for diagnosis and improvement. We propose here a new open source generation method using an evolutionary algorithm to sequentially build molecular graphs. It is independent of starting data and can generate totally unseen compounds. To be able to search a large part of the chemical space, we define an original set of 7 generic mutations close to the atomic level. Our method achieves excellent performances and even records on the QED, penalised logP, SAscore, CLscore as well as the set of goal-directed functions defined in GuacaMol. To demonstrate its flexibility, we tackle a very different objective issued from the organic molecular materials domain. We show that EvoMol can generate sets of optimised molecules having high energy HOMO or low energy LUMO, starting only from methane. We can also set constraints on a synthesizability score and structural features. Finally, the interpretability of EvoMol allows for the visualisation of its exploration process as a chemically relevant tree. ![]()
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Affiliation(s)
- Jules Leguy
- Laboratoire LERIA, UNIV Angers, SFR MathSTIC, 2 Bd Lavoisier, 49045, Angers, France
| | - Thomas Cauchy
- Laboratoire MOLTECH-Anjou, UMR CNRS 6200, UNIV Angers, SFR MATRIX, 2 Bd Lavoisier, 49045, Angers, France.
| | - Marta Glavatskikh
- Laboratoire LERIA, UNIV Angers, SFR MathSTIC, 2 Bd Lavoisier, 49045, Angers, France.,Laboratoire MOLTECH-Anjou, UMR CNRS 6200, UNIV Angers, SFR MATRIX, 2 Bd Lavoisier, 49045, Angers, France
| | - Béatrice Duval
- Laboratoire LERIA, UNIV Angers, SFR MathSTIC, 2 Bd Lavoisier, 49045, Angers, France
| | - Benoit Da Mota
- Laboratoire LERIA, UNIV Angers, SFR MathSTIC, 2 Bd Lavoisier, 49045, Angers, France.
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Mayburd A. A public-private partnership for the express development of antiviral leads: a perspective view. Expert Opin Drug Discov 2020; 16:23-38. [PMID: 32877233 DOI: 10.1080/17460441.2020.1811676] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Abstract
INTRODUCTION The COVID-19 pandemic raises the question of strategic readiness for emergent pathogens. The current case illustrates that the cost of inaction can be higher in the future. The perspective article proposes a dedicated, government-sponsored agency developing anti-viral leads against all potentially dangerous pathogen species. AREAS COVERED The author explores the methods of computational drug screening and in-silico synthesis and proposes a specialized government-sponsored agency focusing on leads and functioning in collaboration with a network of labs, pharma, biotech firms, and academia, in order to test each lead against multiple viral species. The agency will employ artificial intelligence and machine learning tools to cut the costs further. The algorithms are expected to receive continuous feedback from the network of partners conducting the tests. EXPERT OPINION The author proposes a bionic principle, emulating antibody response by producing a combinatorial diversity of high q uality generic antiviral leads, suitable for multiple potentially emerging species. The availability of multiple pre-tested agents and an even greater number of combinations would reduce the impact of the next outbreak. The methodologies developed in this effort are likely to find utility in the design of chronic disease therapeutics.
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Affiliation(s)
- Anatoly Mayburd
- School of Systems Biology, George Mason University , Manassas, USA
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Arya H, Coumar MS. Design of novel ROCK inhibitors using fragment-based de novo drug design approach. J Mol Model 2020; 26:249. [PMID: 32829478 DOI: 10.1007/s00894-020-04493-3] [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: 03/27/2020] [Accepted: 07/30/2020] [Indexed: 12/01/2022]
Abstract
Rho-associated coiled-coil protein kinase (ROCK) is playing a vital role in the regulation of key cellular events and also responsible for causing several pathological conditions such as cancer, hypertension, Alzheimer's, cerebral vasospasm, and cardiac stroke. Therefore, it has attracted us to target ROCK protein as a potential therapeutic target for combating various diseases. Consequently, we investigated the active site of ROCK I protein and designed novel leads against the target using the de novo evolution drug design approach. Caffeic acid (an aglycone of acteoside) as a scaffold and fragments from 336 reported ROCK inhibitors were used for the design of novel leads. Multiple copy simultaneous search docking was used to identify the suitable fragments to be linked with the scaffold. Basic medicinal chemistry rules, coupled with structural insights generated by docking, led to the design of 7a, 8a, 9a, and 10a as potential ROCK I inhibitors. The designed leads showed better binding than the approved drug fasudil and also interacted with the key hinge region residue Met156 of ROCK I. Further, molecular dynamics (MD) simulation revealed that the protein-ligand complexes were stable and maintained the hydrogen bond with Met156 throughout the MD run. The promising in silico outcomes suggest that the designed compounds could be suitable anti-cancer leads that need to be synthesized and tested in various cancer cell lines. Graphical abstract.
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Affiliation(s)
- Hemant Arya
- Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Kalapet, Puducherry, 605014, India
| | - Mohane Selvaraj Coumar
- Centre for Bioinformatics, School of Life Sciences, Pondicherry University, Kalapet, Puducherry, 605014, India.
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Joo S, Kim MS, Yang J, Park J. Generative Model for Proposing Drug Candidates Satisfying Anticancer Properties Using a Conditional Variational Autoencoder. ACS OMEGA 2020; 5:18642-18650. [PMID: 32775866 PMCID: PMC7407547 DOI: 10.1021/acsomega.0c01149] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Accepted: 07/07/2020] [Indexed: 05/08/2023]
Abstract
Deep learning-based molecular generative models have successfully identified drug candidates with desired properties against biological targets of interest. However, syntactically invalid molecules generated from a deep learning-generated model hinder the model from being applied to drug discovery. Herein, we propose a conditional variational autoencoder (CVAE) as a generative model to propose drug candidates with the desired property outside a data set range. We train the CVAE using molecular fingerprints and corresponding GI50 (inhibition of growth by 50%) results for breast cancer cell lines instead of training with various physical properties for each molecule together. We confirm that the generated fingerprints, not included in the training data set, represent the desired property using the CVAE model. In addition, our method can be used as a query expansion method for searching databases because fingerprints generated using our method can be regarded as expanded queries.
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Maia EHB, Assis LC, de Oliveira TA, da Silva AM, Taranto AG. Structure-Based Virtual Screening: From Classical to Artificial Intelligence. Front Chem 2020; 8:343. [PMID: 32411671 PMCID: PMC7200080 DOI: 10.3389/fchem.2020.00343] [Citation(s) in RCA: 209] [Impact Index Per Article: 52.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Accepted: 04/01/2020] [Indexed: 12/15/2022] Open
Abstract
The drug development process is a major challenge in the pharmaceutical industry since it takes a substantial amount of time and money to move through all the phases of developing of a new drug. One extensively used method to minimize the cost and time for the drug development process is computer-aided drug design (CADD). CADD allows better focusing on experiments, which can reduce the time and cost involved in researching new drugs. In this context, structure-based virtual screening (SBVS) is robust and useful and is one of the most promising in silico techniques for drug design. SBVS attempts to predict the best interaction mode between two molecules to form a stable complex, and it uses scoring functions to estimate the force of non-covalent interactions between a ligand and molecular target. Thus, scoring functions are the main reason for the success or failure of SBVS software. Many software programs are used to perform SBVS, and since they use different algorithms, it is possible to obtain different results from different software using the same input. In the last decade, a new technique of SBVS called consensus virtual screening (CVS) has been used in some studies to increase the accuracy of SBVS and to reduce the false positives obtained in these experiments. An indispensable condition to be able to utilize SBVS is the availability of a 3D structure of the target protein. Some virtual databases, such as the Protein Data Bank, have been created to store the 3D structures of molecules. However, sometimes it is not possible to experimentally obtain the 3D structure. In this situation, the homology modeling methodology allows the prediction of the 3D structure of a protein from its amino acid sequence. This review presents an overview of the challenges involved in the use of CADD to perform SBVS, the areas where CADD tools support SBVS, a comparison between the most commonly used tools, and the techniques currently used in an attempt to reduce the time and cost in the drug development process. Finally, the final considerations demonstrate the importance of using SBVS in the drug development process.
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Affiliation(s)
- Eduardo Habib Bechelane Maia
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil.,Federal Center for Technological Education of Minas Gerais-CEFET-MG, Belo Horizonte, Brazil
| | - Letícia Cristina Assis
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil
| | | | | | - Alex Gutterres Taranto
- Laboratory of Pharmaceutical Medicinal Chemistry, Federal University of São João Del Rei, Divinópolis, Brazil
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Yoshimori A, Kawasaki E, Kanai C, Tasaka T. Strategies for Design of Molecular Structures with a Desired Pharmacophore Using Deep Reinforcement Learning. Chem Pharm Bull (Tokyo) 2020; 68:227-233. [DOI: 10.1248/cpb.c19-00625] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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Kriegman S, Blackiston D, Levin M, Bongard J. A scalable pipeline for designing reconfigurable organisms. Proc Natl Acad Sci U S A 2020; 117:1853-1859. [PMID: 31932426 PMCID: PMC6994979 DOI: 10.1073/pnas.1910837117] [Citation(s) in RCA: 134] [Impact Index Per Article: 33.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Living systems are more robust, diverse, complex, and supportive of human life than any technology yet created. However, our ability to create novel lifeforms is currently limited to varying existing organisms or bioengineering organoids in vitro. Here we show a scalable pipeline for creating functional novel lifeforms: AI methods automatically design diverse candidate lifeforms in silico to perform some desired function, and transferable designs are then created using a cell-based construction toolkit to realize living systems with the predicted behaviors. Although some steps in this pipeline still require manual intervention, complete automation in future would pave the way to designing and deploying unique, bespoke living systems for a wide range of functions.
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Affiliation(s)
- Sam Kriegman
- Department of Computer Science, University of Vermont, Burlington, VT 05405
| | - Douglas Blackiston
- Department of Biology, Tufts University, Medford, MA 02153
- Allen Discovery Center, Tufts University, Medford, MA 02153
| | - Michael Levin
- Department of Biology, Tufts University, Medford, MA 02153
- Allen Discovery Center, Tufts University, Medford, MA 02153
- Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA 02115
| | - Josh Bongard
- Department of Computer Science, University of Vermont, Burlington, VT 05405;
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Grisoni F, Moret M, Lingwood R, Schneider G. Bidirectional Molecule Generation with Recurrent Neural Networks. J Chem Inf Model 2020; 60:1175-1183. [PMID: 31904964 DOI: 10.1021/acs.jcim.9b00943] [Citation(s) in RCA: 86] [Impact Index Per Article: 21.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Recurrent neural networks (RNNs) are able to generate de novo molecular designs using simplified molecular input line entry systems (SMILES) string representations of the chemical structure. RNN-based structure generation is usually performed unidirectionally, by growing SMILES strings from left to right. However, there is no natural start or end of a small molecule, and SMILES strings are intrinsically nonunivocal representations of molecular graphs. These properties motivate bidirectional structure generation. Here, bidirectional generative RNNs for SMILES-based molecule design are introduced. To this end, two established bidirectional methods were implemented, and a new method for SMILES string generation and data augmentation is introduced-the bidirectional molecule design by alternate learning (BIMODAL). These three bidirectional strategies were compared to the unidirectional forward RNN approach for SMILES string generation, in terms of the (i) novelty, (ii) scaffold diversity, and (iii) chemical-biological relevance of the computer-generated molecules. The results positively advocate bidirectional strategies for SMILES-based molecular de novo design, with BIMODAL showing superior results to the unidirectional forward RNN for most of the criteria in the tested conditions. The code of the methods and the pretrained models can be found at URL https://github.com/ETHmodlab/BIMODAL.
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Affiliation(s)
- Francesca Grisoni
- Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland
| | - Michael Moret
- Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland
| | - Robin Lingwood
- Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland
| | - Gisbert Schneider
- Department of Chemistry and Applied Biosciences, RETHINK, ETH Zurich, Vladimir-Prelog-Weg 4, 8093 Zurich, Switzerland
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Neural-based approaches to overcome feature selection and applicability domain in drug-related property prediction. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105777] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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50
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Acúrcio RC, Leonardo-Sousa C, García-Sosa AT, Salvador JA, Florindo HF, Guedes RC. Structural insights and binding analysis for determining the molecular bases for programmed cell death protein ligand-1 inhibition. MEDCHEMCOMM 2019; 10:1810-1818. [PMID: 31814954 PMCID: PMC6839816 DOI: 10.1039/c9md00326f] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Accepted: 07/24/2019] [Indexed: 11/21/2022]
Abstract
Programmed cell death protein 1 (PD-1) and PD-ligand 1 (PD-L1) interaction plays an important role in cancer immunotherapy. Several PD-1/PD-L1 inhibitors have been approved with remarkable impact on overall patient survival rates. Inhibitors in clinical practice are presently limited to monoclonal antibodies. However, their severe shortcomings expose the need for a new generation of PD-L1 inhibitors. Understanding the tumor microenvironment, identifying specific biomarkers and X-ray crystalline structures of PD-1/PD-L1 complexes, including molecular and genomic signature studies are essential to determine the success for the development of PD-1/PD-L1 inhibitors into safer and efficient cancer immunotherapeutics. Currently, the development of immune-modulatory small molecules is being explored due to their benefits over recombinant protein approaches. Nevertheless, their development is hampered in part due to lack of structural information. The current study builds on PD-L1 small-molecule inhibitor structural information and provides insights into the design of new inhibitors. To this end, a comprehensive analysis of crystallographic structures and benchmarking studies were performed, showing the specific structure model and software best suited to study PD-L1. The use of in silico methodologies can give a deeper insight to guide the design of novel PD-L1 small-molecule inhibitors.
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Affiliation(s)
- Rita C Acúrcio
- Research Institute for Medicines (iMed.ULisboa) , Faculty of Pharmacy , Universidade de Lisboa , 1649-003 Lisbon , Portugal .
| | - Carlota Leonardo-Sousa
- Research Institute for Medicines (iMed.ULisboa) , Faculty of Pharmacy , Universidade de Lisboa , 1649-003 Lisbon , Portugal .
| | | | - Jorge A Salvador
- Center for Neurosciences and Cell Biology , Universidade de Coimbra , Coimbra , Portugal
| | - Helena F Florindo
- Research Institute for Medicines (iMed.ULisboa) , Faculty of Pharmacy , Universidade de Lisboa , 1649-003 Lisbon , Portugal .
| | - Rita C Guedes
- Research Institute for Medicines (iMed.ULisboa) , Faculty of Pharmacy , Universidade de Lisboa , 1649-003 Lisbon , Portugal .
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