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Alberga D, Lamanna G, Graziano G, Delre P, Lomuscio MC, Corriero N, Ligresti A, Siliqi D, Saviano M, Contino M, Stefanachi A, Mangiatordi GF. DeLA-DrugSelf: Empowering multi-objective de novo design through SELFIES molecular representation. Comput Biol Med 2024; 175:108486. [PMID: 38653065 DOI: 10.1016/j.compbiomed.2024.108486] [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: 02/03/2024] [Revised: 04/08/2024] [Accepted: 04/15/2024] [Indexed: 04/25/2024]
Abstract
In this paper, we introduce DeLA-DrugSelf, an upgraded version of DeLA-Drug [J. Chem. Inf. Model. 62 (2022) 1411-1424], which incorporates essential advancements for automated multi-objective de novo design. Unlike its predecessor, which relies on SMILES notation for molecular representation, DeLA-DrugSelf employs a novel and robust molecular representation string named SELFIES (SELF-referencing Embedded String). The generation process in DeLA-DrugSelf not only involves substitutions to the initial string representing the starting query molecule but also incorporates insertions and deletions. This enhancement makes DeLA-DrugSelf significantly more adept at executing data-driven scaffold decoration and lead optimization strategies. Remarkably, DeLA-DrugSelf explicitly addresses the SELFIES-related collapse issue, considering only collapse-free compounds during generation. These compounds undergo a rigorous quality metrics evaluation, highlighting substantial advancements in terms of drug-likeness, uniqueness, and novelty compared to the molecules generated by the previous version of the algorithm. To evaluate the potential of DeLA-DrugSelf as a mutational operator within a genetic algorithm framework for multi-objective optimization, we employed a fitness function based on Pareto dominance. Our objectives focused on target-oriented properties aimed at optimizing known cannabinoid receptor 2 (CB2R) ligands. The results obtained indicate that DeLA-DrugSelf, available as a user-friendly web platform (https://www.ba.ic.cnr.it/softwareic/delaself/), can effectively contribute to the data-driven optimization of starting bioactive molecules based on user-defined parameters.
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Affiliation(s)
- Domenico Alberga
- CNR - Institute of Crystallography, Via Amendola 122/o, 70126, Bari, Italy
| | - Giuseppe Lamanna
- CNR - Institute of Crystallography, Via Amendola 122/o, 70126, Bari, Italy
| | - Giovanni Graziano
- Department of Pharmacy - Pharmaceutical Sciences, University of Bari "Aldo Moro", via E. Orabona, 4, I-70125, Bari, Italy
| | - Pietro Delre
- CNR - Institute of Crystallography, Via Amendola 122/o, 70126, Bari, Italy
| | | | - Nicola Corriero
- CNR - Institute of Crystallography, Via Amendola 122/o, 70126, Bari, Italy
| | - Alessia Ligresti
- CNR - Institute of Biomolecular Chemistry, Via Campi Flegrei 34, 80078, Pozzuoli, Italy
| | - Dritan Siliqi
- CNR - Institute of Crystallography, Via Amendola 122/o, 70126, Bari, Italy
| | - Michele Saviano
- CNR - Institute of Crystallography, Via Vivaldi 43, 81100, Caserta, Italy
| | - Marialessandra Contino
- Department of Pharmacy - Pharmaceutical Sciences, University of Bari "Aldo Moro", via E. Orabona, 4, I-70125, Bari, Italy
| | - Angela Stefanachi
- Department of Pharmacy - Pharmaceutical Sciences, University of Bari "Aldo Moro", via E. Orabona, 4, I-70125, Bari, Italy
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Mastrolorito F, Togo MV, Gambacorta N, Trisciuzzi D, Giannuzzi V, Bonifazi F, Liantonio A, Imbrici P, De Luca A, Altomare CD, Ciriaco F, Amoroso N, Nicolotti O. TISBE: A Public Web Platform for the Consensus-Based Explainable Prediction of Developmental Toxicity. Chem Res Toxicol 2024; 37:323-339. [PMID: 38200616 DOI: 10.1021/acs.chemrestox.3c00310] [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: 01/12/2024]
Abstract
Despite being extremely relevant for the protection of prenatal and neonatal health, the developmental toxicity (Dev Tox) is a highly complex endpoint whose molecular rationale is still largely unknown. The lack of availability of high-quality data as well as robust nontesting methods makes its understanding even more difficult. Thus, the application of new explainable alternative methods is of utmost importance, with Dev Tox being one of the most animal-intensive research themes of regulatory toxicology. Descending from TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), the present work describes TISBE (TIRESIA Improved on Structure-Based Explainability), a new public web platform implementing four fundamental advancements for in silico analyses: a three times larger dataset, a transparent XAI (explainable artificial intelligence) framework employing a fragment-based fingerprint coding, a novel consensus classifier based on five independent machine learning models, and a new applicability domain (AD) method based on a double top-down approach for better estimating the prediction reliability. The training set (TS) includes as many as 1008 chemicals annotated with experimental toxicity values. Based on a 5-fold cross-validation, a median value of 0.410 for the Matthews correlation coefficient was calculated; TISBE was very effective, with a median value of sensitivity and specificity equal to 0.984 and 0.274, respectively. TISBE was applied on two external pools made of 1484 bioactive compounds and 85 pediatric drugs taken from ChEMBL (Chemical European Molecular Biology Laboratory) and TEDDY (Task-Force in Europe for Drug Development in the Young) repositories, respectively. Notably, TISBE gives users the option to clearly spot the molecular fragments responsible for the toxicity or the safety of a given chemical query and is available for free at https://prometheus.farmacia.uniba.it/tisbe.
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Affiliation(s)
- Fabrizio Mastrolorito
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Maria Vittoria Togo
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Nicola Gambacorta
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Viviana Giannuzzi
- Fondazione per la Ricerca Farmacologica Gianni Benzi Onlus, 70010 Valenzano (BA), Italy
| | - Fedele Bonifazi
- Fondazione per la Ricerca Farmacologica Gianni Benzi Onlus, 70010 Valenzano (BA), Italy
| | - Antonella Liantonio
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Paola Imbrici
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Annamaria De Luca
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Cosimo Damiano Altomare
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Fulvio Ciriaco
- Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
| | - Nicola Amoroso
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125 Bari, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125 Bari, Italy
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Olmedo DA, Durant-Archibold AA, López-Pérez JL, Medina-Franco JL. Design and Diversity Analysis of Chemical Libraries in Drug Discovery. Comb Chem High Throughput Screen 2024; 27:502-515. [PMID: 37409545 DOI: 10.2174/1386207326666230705150110] [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: 04/05/2023] [Revised: 05/30/2023] [Accepted: 05/30/2023] [Indexed: 07/07/2023]
Abstract
Chemical libraries and compound data sets are among the main inputs to start the drug discovery process at universities, research institutes, and the pharmaceutical industry. The approach used in the design of compound libraries, the chemical information they possess, and the representation of structures, play a fundamental role in the development of studies: chemoinformatics, food informatics, in silico pharmacokinetics, computational toxicology, bioinformatics, and molecular modeling to generate computational hits that will continue the optimization process of drug candidates. The prospects for growth in drug discovery and development processes in chemical, biotechnological, and pharmaceutical companies began a few years ago by integrating computational tools with artificial intelligence methodologies. It is anticipated that it will increase the number of drugs approved by regulatory agencies shortly.
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Affiliation(s)
- Dionisio A Olmedo
- Centro de Investigaciones Farmacognósticas de la Flora Panameña (CIFLORPAN), Facultad de Farmacia, Universidad de Panamá, Ciudad de Panamá, Apartado, 0824-00178, Panamá
- Sistema Nacional de Investigación (SNI), Secretaria Nacional de Ciencia, Tecnología e Innovación (SENACYT), Ciudad del Saber, Clayton, Panamá
| | - Armando A Durant-Archibold
- Centro de Biodiversidad y Descubrimiento de Drogas, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Apartado, 0843-01103, Panamá
- Departamento de Bioquímica, Facultad de Ciencias Naturales, Exactas y Tecnología, Universidad de Panamá, Ciudad de Panamá, Panamá
| | - José Luis López-Pérez
- CESIFAR, Departamento de Farmacología, Facultad de Medicina, Universidad de Panamá, Ciudad de Panamá, Panamá
- Departamento de Ciencias Farmacéuticas, Facultad de Farmacia, Universidad de Salamanca, Avda. Campo Charro s/n, 37071 Salamanca, España
| | - José Luis Medina-Franco
- DIFACQUIM Grupo de Investigación, Departamento de Farmacia, Escuela de Química, Universidad Nacional Autónoma de México, Ciudad de México, Apartado, 04510, México
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Vittoria Togo M, Mastrolorito F, Orfino A, Graps EA, Tondo AR, Altomare CD, Ciriaco F, Trisciuzzi D, Nicolotti O, Amoroso N. Where developmental toxicity meets explainable artificial intelligence: state-of-the-art and perspectives. Expert Opin Drug Metab Toxicol 2023:1-17. [PMID: 38141160 DOI: 10.1080/17425255.2023.2298827] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 12/20/2023] [Indexed: 12/24/2023]
Abstract
INTRODUCTION The application of Artificial Intelligence (AI) to predictive toxicology is rapidly increasing, particularly aiming to develop non-testing methods that effectively address ethical concerns and reduce economic costs. In this context, Developmental Toxicity (Dev Tox) stands as a key human health endpoint, especially significant for safeguarding maternal and child well-being. AREAS COVERED This review outlines the existing methods employed in Dev Tox predictions and underscores the benefits of utilizing New Approach Methodologies (NAMs), specifically focusing on eXplainable Artificial Intelligence (XAI), which proves highly efficient in constructing reliable and transparent models aligned with recommendations from international regulatory bodies. EXPERT OPINION The limited availability of high-quality data and the absence of dependable Dev Tox methodologies render XAI an appealing avenue for systematically developing interpretable and transparent models, which hold immense potential for both scientific evaluations and regulatory decision-making.
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Affiliation(s)
- Maria Vittoria Togo
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Fabrizio Mastrolorito
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Angelica Orfino
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Elisabetta Anna Graps
- ARESS Puglia - Agenzia Regionale strategica per laSalute ed il Sociale, Presidenza della Regione Puglia", Bari, Italy
| | - Anna Rita Tondo
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Cosimo Damiano Altomare
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Fulvio Ciriaco
- Department of Chemistry, Universitá degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Daniela Trisciuzzi
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Orazio Nicolotti
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
| | - Nicola Amoroso
- Department of Pharmacy - Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", Bari, Italy
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García-Castro M, Fuentes-Rios D, López-Romero JM, Romero A, Moya-Utrera F, Díaz-Morilla A, Sarabia F. n-Tuples on Scaffold Diversity Inspired by Drug Hybridisation to Enhance Drugability: Application to Cytarabine. Mar Drugs 2023; 21:637. [PMID: 38132958 PMCID: PMC10744741 DOI: 10.3390/md21120637] [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: 10/12/2023] [Revised: 12/07/2023] [Accepted: 12/11/2023] [Indexed: 12/23/2023] Open
Abstract
A mathematical concept, n-tuples are originally applied to medicinal chemistry, especially with the creation of scaffold diversity inspired by the hybridisation of different commercial drugs with cytarabine, a synthetic arabinonucleoside derived from two marine natural products, spongouridine and spongothymidine. The new methodology explores the virtual chemical-factorial combination of different commercial drugs (immunosuppressant, antibiotic, antiemetic, anti-inflammatory, and anticancer) with the anticancer drug cytarabine. Real chemical combinations were designed and synthesised for 8-duples, obtaining a small representative library of interesting organic molecules to be biologically tested as proof of concept. The synthesised library contains classical molecular properties regarding the Lipinski rules and/or beyond rules of five (bRo5) and is represented by the covalent combination of the anticancer drug cytarabine with ibuprofen, flurbiprofen, folic acid, sulfasalazine, ciprofloxacin, bortezomib, and methotrexate. The insertion of specific nomenclature could be implemented into artificial intelligence algorithms in order to enhance the efficiency of drug-hunting programs. The novel methodology has proven useful for the straightforward synthesis of most of the theoretically proposed duples and, in principle, could be extended to any other central drug.
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Affiliation(s)
- Miguel García-Castro
- Department of Organic Chemistry, Faculty of Sciences, University of Malaga, Campus de Teatinos s/n, 29071 Malaga, Spain
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Amoroso N, Gambacorta N, Mastrolorito F, Togo MV, Trisciuzzi D, Monaco A, Pantaleo E, Altomare CD, Ciriaco F, Nicolotti O. Making sense of chemical space network shows signs of criticality. Sci Rep 2023; 13:21335. [PMID: 38049451 PMCID: PMC10696027 DOI: 10.1038/s41598-023-48107-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 11/22/2023] [Indexed: 12/06/2023] Open
Abstract
Chemical space modelling has great importance in unveiling and visualising latent information, which is critical in predictive toxicology related to drug discovery process. While the use of traditional molecular descriptors and fingerprints may suffer from the so-called curse of dimensionality, complex networks are devoid of the typical drawbacks of coordinate-based representations. Herein, we use chemical space networks (CSNs) to analyse the case of the developmental toxicity (Dev Tox), which remains a challenging endpoint for the difficulty of gathering enough reliable data despite very important for the protection of the maternal and child health. Our study proved that the Dev Tox CSN has a complex non-random organisation and can thus provide a wealth of meaningful information also for predictive purposes. At a phase transition, chemical similarities highlight well-established toxicophores, such as aryl derivatives, mostly neurotoxic hydantoins, barbiturates and amino alcohols, steroids, and volatile organic compounds ether-like chemicals, which are strongly suspected of the Dev Tox onset and can thus be employed as effective alerts for prioritising chemicals before testing.
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Affiliation(s)
- Nicola Amoroso
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy.
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, via E. Orabona, 4, 70125, Bari, Italy.
| | - Nicola Gambacorta
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
- Division of Medical Genetics, Fondazione IRCCS-Casa Sollievo della Sofferenza, San Giovanni Rotondo (Foggia), Italy
| | - Fabrizio Mastrolorito
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
| | - Maria Vittoria Togo
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, via E. Orabona, 4, 70125, Bari, Italy
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli studi di Bari Aldo Moro, Via Giovanni Amendola, 173, 70125, Bari, Italy
| | - Ester Pantaleo
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, via E. Orabona, 4, 70125, Bari, Italy
- Dipartimento Interateneo di Fisica "M. Merlin", Università degli studi di Bari Aldo Moro, Via Giovanni Amendola, 173, 70125, Bari, Italy
| | - Cosimo Damiano Altomare
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
| | - Fulvio Ciriaco
- Dipartimento di Chimica, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy.
| | - Orazio Nicolotti
- Dipartimento di Farmacia - Scienze del Farmaco, Università degli studi di Bari Aldo Moro, via E. Orabona, 4, 70125, Bari, Italy
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Alzain AA, Elbadwi FA, Mukhtar RM, Shoaib TH, Abdelmoniem N, Miski SF, Ghazawi KF, Alsulaimany M, Mohamed SGA, Ainousah BE, Hussein HGA, Mohamed GA, Ibrahim SRM. Design of new Mcl-1 inhibitors for cancer using fragments hybridization, molecular docking, and molecular dynamics studies. J Biomol Struct Dyn 2023:1-13. [PMID: 37962580 DOI: 10.1080/07391102.2023.2281637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 11/04/2023] [Indexed: 11/15/2023]
Abstract
Apoptosis is a critical process that regulates cell survival and death and plays an essential role in cancer development. The Bcl-2 protein family, including myeloid leukemia 1 (Mcl-1), is a key regulator of the intrinsic apoptosis pathway, and its overexpression in many human cancers has prompted efforts to develop Mcl-1 inhibitors as potential anticancer agents. In this study, we aimed to design new Mcl-1 inhibitors using various computational techniques. First, we used the Mcl-1 receptor-ligand complex to build an e-pharmacophore hypothesis and screened a library of 567,000 fragments from the Enamine database. We obtained 410 fragments and used them to design 92,384 novel compounds, which we then docked into the Mcl-1 binding cavity using HTVS, SP, and XP docking modes of Glide. To assess their suitability as drug candidates, we conducted MM-GBSA calculations and ADME prediction, leading to the identification of 10 compounds with excellent binding affinity and favorable pharmacokinetic properties. To further investigate the interaction strength, we performed molecular dynamics simulations on the top three Mcl-1 receptor-ligand complexes to study their interaction stability. Overall, our findings suggest that these compounds have promising potential as anticancer agents, pending further experimental validation such as Mcl-1 apoptosis Assay. By combining experimental methods with various in silico approaches, these techniques prove to be invaluable for identifying novel drug candidates with distinct therapeutic applications using fragment-based drug design. This methodology has the potential to expedite the drug discovery process while also reducing its costs.Communicated by Ramaswamy H. Sarma.
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Affiliation(s)
- Abdulrahim A Alzain
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Gezira, Wad Madani, Sudan
| | - Fatima A Elbadwi
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Gezira, Wad Madani, Sudan
| | - Rua M Mukhtar
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Gezira, Wad Madani, Sudan
| | - Tagyedeen H Shoaib
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Gezira, Wad Madani, Sudan
| | - Nihal Abdelmoniem
- Department of Pharmaceutical Chemistry, Faculty of Pharmacy, University of Gezira, Wad Madani, Sudan
| | - Samar F Miski
- Department of Pharmacology and Toxicology, College of Pharmacy, Taibah University, Al-Madinah Al-Munawwarah, Saudi Arabia
| | - Kholoud F Ghazawi
- Pharmacy Practice Department, College of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Marwa Alsulaimany
- Department of Pharmacognosy & Pharmaceutical Chemistry, College of Pharmacy, Taibah University, Medina, Saudi Arabia
| | | | - Bayan E Ainousah
- Department of Pharmaceutical Sciences, Faculty of Pharmacy, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Hazem G A Hussein
- Preparatory Year Program, Batterjee Medical College, Jeddah, Saudi Arabia
| | - Gamal A Mohamed
- Department of Natural Products and Alternative Medicine, Faculty of Pharmacy, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Sabrin R M Ibrahim
- Preparatory Year Program, Department of Chemistry, Batterjee Medical College, Jeddah, Saudi Arabia
- Department of Pharmacognosy, Faculty of Pharmacy, Assiut University, Assiut, Egypt
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Gambacorta N, Gasperi V, Guzzo T, Di Leva FS, Ciriaco F, Sánchez C, Tullio V, Rozzi D, Marinelli L, Topai A, Nicolotti O, Maccarrone M. Exploring the 1,3-benzoxazine chemotype for cannabinoid receptor 2 as a promising anti-cancer therapeutic. Eur J Med Chem 2023; 259:115647. [PMID: 37478557 DOI: 10.1016/j.ejmech.2023.115647] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/12/2023] [Accepted: 07/13/2023] [Indexed: 07/23/2023]
Abstract
The discovery of selective agonists of cannabinoid receptor 2 (CB2) is strongly pursued to successfully tuning endocannabinoid signaling for therapeutic purposes. However, the design of selective CB2 agonists is still challenging because of the high homology with the cannabinoid receptor 1 (CB1) and for the yet unclear molecular basis of the agonist/antagonist switch. Here, the 1,3-benzoxazine scaffold is presented as a versatile chemotype for the design of CB2 agonists from which 25 derivatives were synthesized. Among these, compound 7b5 (CB2 EC50 = 110 nM, CB1 EC50 > 10 μM) demonstrated to impair proliferation of triple negative breast cancer BT549 cells and to attenuate the release of pro-inflammatory cytokines in a CB2-dependent manner. Furthermore, 7b5 abrogated the activation of extracellular signal-regulated kinase (ERK) 1/2, a key pro-inflammatory and oncogenic enzyme. Finally, molecular dynamics studies suggested a new rationale for the in vitro measured selectivity and for the observed agonist behavior.
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Affiliation(s)
- Nicola Gambacorta
- Department of Pharmacy-Pharmaceutical Sciences, University of the Studies of Bari "Aldo Moro", Via E. Orabona 4, 70125, Bari, Italy
| | - Valeria Gasperi
- Department of Experimental Medicine, Tor Vergata University of Rome, Via Montpellier 1, 00133, Rome, Italy
| | - Tatiana Guzzo
- C4T S.r.l Colosseum Combinatorial Chemistry Centre for Technology, Via Della Ricerca Scientifica Snc, 00133, Rome, Italy
| | | | - Fulvio Ciriaco
- Department of Chemistry, University of the Studies of Bari "Aldo Moro", Via E. Orabona 4, 70125, Bari, Italy
| | - Cristina Sánchez
- Department of Biochemistry and Molecular Biology, School of Biology, Complutense University, C/ José Antonio Nováis, 12, 28040, Madrid, Spain
| | - Valentina Tullio
- Department of Experimental Medicine, Tor Vergata University of Rome, Via Montpellier 1, 00133, Rome, Italy
| | - Diego Rozzi
- C4T S.r.l Colosseum Combinatorial Chemistry Centre for Technology, Via Della Ricerca Scientifica Snc, 00133, Rome, Italy
| | - Luciana Marinelli
- Department of Pharmacy, University of Naples Federico II, Via D. Montesano 49, 80131, Naples, Italy
| | - Alessandra Topai
- C4T S.r.l Colosseum Combinatorial Chemistry Centre for Technology, Via Della Ricerca Scientifica Snc, 00133, Rome, Italy.
| | - Orazio Nicolotti
- Department of Pharmacy-Pharmaceutical Sciences, University of the Studies of Bari "Aldo Moro", Via E. Orabona 4, 70125, Bari, Italy.
| | - Mauro Maccarrone
- Department of Biotechnological and Applied Clinical Sciences, University of L'Aquila, Via Vetoio, 67100, Coppito, L'Aquila, Italy; European Center for Brain Research/Santa Lucia Foundation IRCCS, Via Del Fosso di Fiorano 64, 00143, Rome, Italy.
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9
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Matos GDR, Pak S, Rizzo RC. Descriptor-Driven de Novo Design Algorithms for DOCK6 Using RDKit. J Chem Inf Model 2023; 63:5803-5822. [PMID: 37698425 PMCID: PMC10694857 DOI: 10.1021/acs.jcim.3c01031] [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: 09/13/2023]
Abstract
Structure-based methods that employ principles of de novo design can be used to construct small organic molecules from scratch using pre-existing fragment libraries to sample chemical space and are an important class of computational algorithms for drug-lead discovery. Here, we present a powerful new design method for DOCK6 that employs a Descriptor-Driven De Novo strategy (termed D3N) in which user-defined cheminformatics descriptors (and their target ranges) are calculated at each layer of growth using the open-source toolkit RDKit. The objective is to tailor ligand growth toward desirable regions of chemical space. The approach was extensively validated through: (1) comparison of cheminformatics descriptors computed using the new DOCK6/RDKit interface versus the standard Python/RDKit installation, (2) examination of descriptor distributions generated using D3N growth under different conditions (target ranges and environments), and (3) construction of ligands with very tight (pinpoint) descriptor ranges using clinically relevant compounds as a reference. Our testing confirms that the new DOCK6/RDKit integration is robust, showcases how the new D3N routines can be used to direct sampling around user-defined chemical spaces, and highlights the utility of on-the-fly descriptor calculations for ligand design to important drug targets.
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Affiliation(s)
- Guilherme Duarte Ramos Matos
- Department of Applied Mathematics & Statistics, Stony Brook University, Stony Brook, New York 11794, USA
- Instituto de Química, Universidade de Brasília, Brasília, Distrito Federal, 70910-900, Brazil
| | - Steven Pak
- Department of Pharmacological Sciences, Stony Brook University, Stony Brook, New York, 11794, USA
| | - Robert C. Rizzo
- Department of Applied Mathematics & Statistics, Stony Brook University, Stony Brook, New York 11794, USA
- Institute of Chemical Biology & Drug Discovery, Stony Brook University, Stony Brook, New York 11794, USA
- Laufer Center for Physical & Quantitative Biology, Stony Brook University, Stony Brook, New York 11794, USA
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10
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Yang Y, Hsieh CY, Kang Y, Hou T, Liu H, Yao X. Deep Generation Model Guided by the Docking Score for Active Molecular Design. J Chem Inf Model 2023; 63:2983-2991. [PMID: 37163364 DOI: 10.1021/acs.jcim.3c00572] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
A deep generation model, as a novel drug design and discovery tool, shows obvious advantages in generating compounds with novel backbones and has been applied successfully in the field of drug discovery. However, it is still a challenge to generate molecules with expected properties, especially high activity. Here, to obtain compounds both with novelty and high activity to a target, we proposed a conditional molecular generation model COMG by considering the docking score and 3D pharmacophore matching during molecular generation. The proposed model was based on the conditional variational autoencoder architecture constrained by the pharmacophore matching score. During Bayesian optimization, the docking score was applied to enhance the target relevance of generated compounds. Furthermore, to overcome the problem of high structural similarity caused by Bayesian optimization, the idea of the scaffold memory unit was also introduced. The evaluation results of COMG show that our model not only can improve the structural diversity of generated molecules but also can effectively improve the proportion of target-related drug-active molecules. The obtained results indicate that our proposed model COMG is a useful drug design tool.
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Affiliation(s)
- Yuwei Yang
- Faculty of Applied Sciences, Macao Polytechnic University, Macao (SAR) 999078, P. R. China
- School of Pharmacy, Lanzhou University, Lanzhou 730000, Gansu, P. R. China
| | - Chang-Yu Hsieh
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, P. R. China
| | - Yu Kang
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, P. R. China
| | - Tingjun Hou
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, P. R. China
| | - Huanxiang Liu
- Faculty of Applied Sciences, Macao Polytechnic University, Macao (SAR) 999078, P. R. China
| | - Xiaojun Yao
- State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, 999078 Macau (SAR), P. R. China
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11
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Luukkonen S, van den Maagdenberg HW, Emmerich MTM, van Westen GJP. Artificial intelligence in multi-objective drug design. Curr Opin Struct Biol 2023; 79:102537. [PMID: 36774727 DOI: 10.1016/j.sbi.2023.102537] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 12/21/2022] [Accepted: 01/03/2023] [Indexed: 02/12/2023]
Abstract
The factors determining a drug's success are manifold, making de novo drug design an inherently multi-objective optimisation (MOO) problem. With the advent of machine learning and optimisation methods, the field of multi-objective compound design has seen a rapid increase in developments and applications. Population-based metaheuris-tics and deep reinforcement learning are the most commonly used artificial intelligence methods in the field, but recently conditional learning methods are gaining popularity. The former approaches are coupled with a MOO strat-egy which is most commonly an aggregation function, but Pareto-based strategies are widespread too. Besides these and conditional learning, various innovative approaches to tackle MOO in drug design have been proposed. Here we provide a brief overview of the field and the latest innovations.
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Affiliation(s)
- Sohvi Luukkonen
- Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, Leiden, 2333 CC, the Netherlands. https://twitter.com/sohvi_luukkonen
| | - Helle W van den Maagdenberg
- Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, Leiden, 2333 CC, the Netherlands
| | - Michael T M Emmerich
- Leiden Institute of Advanced Computer Science, Leiden University, Niels Bohrweg 1, Leiden, 2333 CC, the Netherlands
| | - Gerard J P van Westen
- Leiden Academic Centre for Drug Research, Leiden University, Einsteinweg 55, Leiden, 2333 CC, the Netherlands.
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12
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Fromer JC, Coley CW. Computer-aided multi-objective optimization in small molecule discovery. PATTERNS (NEW YORK, N.Y.) 2023; 4:100678. [PMID: 36873904 PMCID: PMC9982302 DOI: 10.1016/j.patter.2023.100678] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Molecular discovery is a multi-objective optimization problem that requires identifying a molecule or set of molecules that balance multiple, often competing, properties. Multi-objective molecular design is commonly addressed by combining properties of interest into a single objective function using scalarization, which imposes assumptions about relative importance and uncovers little about the trade-offs between objectives. In contrast to scalarization, Pareto optimization does not require knowledge of relative importance and reveals the trade-offs between objectives. However, it introduces additional considerations in algorithm design. In this review, we describe pool-based and de novo generative approaches to multi-objective molecular discovery with a focus on Pareto optimization algorithms. We show how pool-based molecular discovery is a relatively direct extension of multi-objective Bayesian optimization and how the plethora of different generative models extend from single-objective to multi-objective optimization in similar ways using non-dominated sorting in the reward function (reinforcement learning) or to select molecules for retraining (distribution learning) or propagation (genetic algorithms). Finally, we discuss some remaining challenges and opportunities in the field, emphasizing the opportunity to adopt Bayesian optimization techniques into multi-objective de novo design.
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Affiliation(s)
- Jenna C Fromer
- Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA
| | - Connor W Coley
- Department of Chemical Engineering, MIT, Cambridge, MA 02139, USA.,Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA 02139, USA
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13
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Kumar M, Nguyen TPN, Kaur J, Singh TG, Soni D, Singh R, Kumar P. Opportunities and challenges in application of artificial intelligence in pharmacology. Pharmacol Rep 2023; 75:3-18. [PMID: 36624355 PMCID: PMC9838466 DOI: 10.1007/s43440-022-00445-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 12/23/2022] [Accepted: 12/25/2022] [Indexed: 01/11/2023]
Abstract
Artificial intelligence (AI) is a machine science that can mimic human behaviour like intelligent analysis of data. AI functions with specialized algorithms and integrates with deep and machine learning. Living in the digital world can generate a huge amount of medical data every day. Therefore, we need an automated and reliable evaluation tool that can make decisions more accurately and faster. Machine learning has the potential to learn, understand and analyse the data used in healthcare systems. In the last few years, AI is known to be employed in various fields in pharmaceutical science especially in pharmacological research. It helps in the analysis of preclinical (laboratory animals) and clinical (in human) trial data. AI also plays important role in various processes such as drug discovery/manufacturing, diagnosis of big data for disease identification, personalized treatment, clinical trial research, radiotherapy, surgical robotics, smart electronic health records, and epidemic outbreak prediction. Moreover, AI has been used in the evaluation of biomarkers and diseases. In this review, we explain various models and general processes of machine learning and their role in pharmacological science. Therefore, AI with deep learning and machine learning could be relevant in pharmacological research.
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Affiliation(s)
- Mandeep Kumar
- Department of Pharmacy, Unit of Pharmacology and Toxicology, University of Genoa, Genoa, Italy
| | - T P Nhung Nguyen
- Department of Pharmacy, Unit of Pharmacology and Toxicology, University of Genoa, Genoa, Italy
- Department of Pharmacy, Da Nang University of Medical Technology and Pharmacy, Da Nang, Vietnam
| | - Jasleen Kaur
- Department of Pharmacology and Toxicology, National Institute of Pharmaceutical Education and Research (NIPER), Lucknow, Uttar Pradesh, 226002, India
| | | | - Divya Soni
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India
| | - Randhir Singh
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India
| | - Puneet Kumar
- Department of Pharmacology, Central University of Punjab, Ghudda, Bathinda, Punjab, 151401, India.
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14
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Carullo G, Falbo F, Ahmed A, Trezza A, Gianibbi B, Nicolotti O, Campiani G, Aiello F, Saponara S, Fusi F. Artificial intelligence-driven identification of morin analogues acting as Ca V1.2 channel blockers: Synthesis and biological evaluation. Bioorg Chem 2023; 131:106326. [PMID: 36563413 DOI: 10.1016/j.bioorg.2022.106326] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 12/02/2022] [Accepted: 12/12/2022] [Indexed: 12/23/2022]
Abstract
Morin is a vasorelaxant flavonoid, whose activity is ascribable to CaV1.2 channel blockade that, however, is weak as compared to that of clinically used therapeutic agents. A conventional strategy to circumvent this drawback is to synthesize new derivatives differently decorated and, in this context, morin-derivatives able to interact with CaV1.2 channels were found by employing the potential of PLATO in target fishing and reverse screening. Three different derivatives (5a-c) were selected as promising tools, synthesized, and investigated in in vitro functional studies using rat aorta rings and rat tail artery myocytes. 5a-c were found more effective vasorelaxant agents than the naturally occurring parent compound and antagonized both electro- and pharmaco-mechanical coupling in an endothelium-independent manner. 5a, the series' most potent, reduced also Ca2+ mobilization from intracellular store sites. Furthermore, 5a≈5c > 5b inhibited Ba2+ current through CaV1.2 channels. However, compound 5a caused also a concentration-dependent inhibition of KCa1.1 channel currents.
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Affiliation(s)
- Gabriele Carullo
- Department of Life Sciences, University of Siena, Via Aldo Moro 2, 53100, Siena, Italy
| | - Federica Falbo
- Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Ed. Polifunzionale, 87036, Rende (CS), Italy
| | - Amer Ahmed
- Department of Life Sciences, University of Siena, Via Aldo Moro 2, 53100, Siena, Italy
| | - Alfonso Trezza
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via Aldo Moro 2, 53100, Siena, Italy
| | - Beatrice Gianibbi
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via Aldo Moro 2, 53100, Siena, Italy
| | - Orazio Nicolotti
- Department of Pharmacy- Drug Sciences, University of Bari "Aldo Moro", Via Orabona 4, 70125 Bari, Italy
| | - Giuseppe Campiani
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via Aldo Moro 2, 53100, Siena, Italy
| | - Francesca Aiello
- Department of Pharmacy, Health and Nutritional Sciences, University of Calabria, Ed. Polifunzionale, 87036, Rende (CS), Italy.
| | - Simona Saponara
- Department of Life Sciences, University of Siena, Via Aldo Moro 2, 53100, Siena, Italy
| | - Fabio Fusi
- Department of Biotechnology, Chemistry and Pharmacy, University of Siena, Via Aldo Moro 2, 53100, Siena, Italy
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15
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Kumar K, Kumar P, Deb D, Unguresan ML, Muresan V. Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends. Healthcare (Basel) 2023; 11:healthcare11020207. [PMID: 36673575 PMCID: PMC9859198 DOI: 10.3390/healthcare11020207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Revised: 01/01/2023] [Accepted: 01/04/2023] [Indexed: 01/13/2023] Open
Abstract
People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. The subject of this technology is the study of how computers may learn from data and imitate human mental processes. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML. This article offers a survey of the uses of AI and ML in the healthcare industry, with a particular emphasis on clinical, developmental, administrative, and global health implementations to support the healthcare infrastructure as a whole, along with the impact and expectations of each component of healthcare. Additionally, possible future trends and scopes of the utilization of this technology in medical infrastructure have also been discussed.
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Affiliation(s)
- Kamlesh Kumar
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research And Management, Ahmedabad 380026, India
| | - Prince Kumar
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research And Management, Ahmedabad 380026, India
| | - Dipankar Deb
- Department of Electrical and Computer Science Engineering, Institute of Infrastructure Technology Research And Management, Ahmedabad 380026, India
- Correspondence:
| | | | - Vlad Muresan
- Department of Automation, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, Romania
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16
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Togo MV, Mastrolorito F, Ciriaco F, Trisciuzzi D, Tondo AR, Gambacorta N, Bellantuono L, Monaco A, Leonetti F, Bellotti R, Altomare CD, Amoroso N, Nicolotti O. TIRESIA: An eXplainable Artificial Intelligence Platform for Predicting Developmental Toxicity. J Chem Inf Model 2023; 63:56-66. [PMID: 36520016 DOI: 10.1021/acs.jcim.2c01126] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Herein, a robust and reproducible eXplainable Artificial Intelligence (XAI) approach is presented, which allows prediction of developmental toxicity, a challenging human-health endpoint in toxicology. The application of XAI as an alternative method is of the utmost importance with developmental toxicity being one of the most animal-intensive areas of regulatory toxicology. In this work, the established CAESAR (Computer Assisted Evaluation of industrial chemical Substances According to Regulations) training set made of 234 chemicals for model learning is employed. Two test sets, including as a whole 585 chemicals, were instead used for validation and generalization purposes. The proposed framework favorably compares with the state-of-the-art approaches in terms of accuracy, sensitivity, and specificity, thus resulting in a reliable support system for developmental toxicity ensuring informativeness, uncertainty estimation, generalization, and transparency. Based on the eXtreme Gradient Boosting (XGB) algorithm, our predictive model provides easy interpretative keys based on specific molecular descriptors and structural alerts enabling one to distinguish toxic and nontoxic chemicals. Inspired by the Organisation for Economic Co-operation and Development (OECD) principles for the validation of Quantitative Structure-Activity Relationships (QSARs) for regulatory purposes, the results are summarized in a standard report in portable document format, enclosing also details concerned with a density-based model applicability domain and SHAP (SHapley Additive exPlanations) explainability, the latter particularly useful to better understand the effective roles played by molecular features. Notably, our model has been implemented in TIRESIA (Toxicology Intelligence and Regulatory Evaluations for Scientific and Industry Applications), a free of charge web platform available at http://tiresia.uniba.it.
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Affiliation(s)
- Maria Vittoria Togo
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Fabrizio Mastrolorito
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Fulvio Ciriaco
- Dipartimento di Chimica, Università degli Studi di Bari Aldo Moro, 70125, Bari, Italy
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Anna Rita Tondo
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Nicola Gambacorta
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Loredana Bellantuono
- Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari Aldo Moro, 70124Bari, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125Bari, Italy
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125Bari, Italy.,Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Francesco Leonetti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125Bari, Italy.,Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Cosimo Damiano Altomare
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
| | - Nicola Amoroso
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy.,Istituto Nazionale di Fisica Nucleare, Sezione di Bari, 70125Bari, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari Aldo Moro, 70125Bari, Italy
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17
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Dong J, Qian J, Yu K, Huang S, Cheng X, Chen F, Jiang H, Zeng W. Rational Design of Organelle-Targeted Fluorescent Probes: Insights from Artificial Intelligence. RESEARCH (WASHINGTON, D.C.) 2023; 6:0075. [PMID: 36930810 PMCID: PMC10013958 DOI: 10.34133/research.0075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 01/18/2023] [Indexed: 01/27/2023]
Abstract
Monitoring the physiological changes of organelles is essential for understanding the local biological information of cells and for improving the diagnosis and therapy of diseases. Currently, fluorescent probes are considered as the most powerful tools for imaging and have been widely applied in biomedical fields. However, the expected targeting effects of these probes are often inconsistent with the real experiments. The design of fluorescent probes mainly depends on the empirical knowledge of researchers, which was inhibited by limited chemical space and low efficiency. Herein, we proposed a novel multilevel framework for the prediction of organelle-targeted fluorescent probes by employing advanced artificial intelligence algorithms. In this way, not only the targeting mechanism could be interpreted beyond intuitions but also a quick evaluation method could be established for the rational design. Furthermore, the targeting and imaging powers of the optimized and synthesized probes based on this methodology were verified by quantitative calculation and experiments.
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Affiliation(s)
- Jie Dong
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410083, P.R. China
| | - Jie Qian
- National Engineering Research Center of Rice and Byproduct Deep Processing, School of Food Science and Engineering, Central South University of Forestry and Technology, Changsha 410004, P.R. China
| | - Kunqian Yu
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, P.R. China
| | - Shuai Huang
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410083, P.R. China
| | - Xiang Cheng
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410083, P.R. China
| | - Fei Chen
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410083, P.R. China
| | - Hualiang Jiang
- State Key Laboratory of Drug Research, Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, P.R. China
| | - Wenbin Zeng
- Xiangya School of Pharmaceutical Sciences, Central South University, Changsha 410083, P.R. China
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18
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Ciriaco F, Gambacorta N, Leonetti F, Altomare CD, Nicolotti O. Virtual Reverse Screening Approach to Target Type 2 Cannabinoid Receptor. Methods Mol Biol 2023; 2576:495-504. [PMID: 36152212 DOI: 10.1007/978-1-0716-2728-0_40] [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] [Indexed: 06/16/2023]
Abstract
A screening pool consisting of 617710 drug-like query molecules properly filtered from the ChEMBL database was employed for a ligand-based reverse screening toward the type 2 cannabinoid receptor (CB2) target. By using our recently developed PLATO polypharmacological web platform, 233 out of 617710 drug-like molecules were prioritized on the basis of the predicted bioactivity values, better than 0.2 μM with a probability of about 98%, toward the CB2 target. Building on these results, the occurrence of putative CB2-related targets was also investigated for prospective repurposing studies.
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Affiliation(s)
- Fulvio Ciriaco
- Department of Chemistry, University of Bari, Bari, Italy
| | - Nicola Gambacorta
- Department of Pharmacy-Pharmaceutical Sciences, University of Bari, Bari, Italy
| | - Francesco Leonetti
- Department of Pharmacy-Pharmaceutical Sciences, University of Bari, Bari, Italy
| | | | - Orazio Nicolotti
- Department of Pharmacy-Pharmaceutical Sciences, University of Bari, Bari, Italy.
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19
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Trisciuzzi D, Siragusa L, Baroni M, Cruciani G, Nicolotti O. An Integrated Machine Learning Model To Spot Peptide Binding Pockets in 3D Protein Screening. J Chem Inf Model 2022; 62:6812-6824. [PMID: 36320100 DOI: 10.1021/acs.jcim.2c00583] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
The prediction of peptide-protein binding sites is of utmost importance to tackle the onset of severe neurodegenerative diseases and cancer. In this work, we detail a novel machine learning model based on Linear Discriminant Analysis (LDA) demonstrating to be highly predictive in detecting the putative protein binding regions of small peptides. Starting from 439 high-quality pockets derived from peptide-protein crystallographic complexes, three sets of well-established peptide-binding regions were first selected through a Partitioning Around Medoids (PAM) clustering algorithm based on morphological and energetic 3D GRID-MIF molecular descriptors. Next, the best combination between all the putative interacting peptide pockets and related GRID-MIF scores was automatically explored by using the LDA-based protocol implemented in BioGPS. This approach proved successful to recognize the actual interacting peptide regions (that is, AUC = 0.86 and partial ROC enrichment at 5% of 0.48) from all the other pockets of the protein. Validated on two external collections sets, including 445 and 347 crystallographic peptide-protein complexes, our LDA-based model could be effective to further run peptide-protein virtual screening campaigns.
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Affiliation(s)
- Daniela Trisciuzzi
- Department of Pharmacy-Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", 70125Bari, Italy.,Molecular Discovery Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, HertfordshireWD6 4PJ, United Kingdom
| | - Lydia Siragusa
- Molecular Horizon s.r.l., Via Montelino, 30, 06084Bettona (PG), Italy.,Molecular Discovery Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, HertfordshireWD6 4PJ, United Kingdom
| | - Massimo Baroni
- Molecular Discovery Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, HertfordshireWD6 4PJ, United Kingdom
| | - Gabriele Cruciani
- Department of Chemistry, Biology and Biotechnology, Università degli Studi di Perugia, via Elce di Sotto, 8, 06123Perugia (PG), Italy
| | - Orazio Nicolotti
- Department of Pharmacy-Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", 70125Bari, Italy
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20
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Tan Y, Dai L, Huang W, Guo Y, Zheng S, Lei J, Chen H, Yang Y. DRlinker: Deep Reinforcement Learning for Optimization in Fragment Linking Design. J Chem Inf Model 2022; 62:5907-5917. [PMID: 36404642 DOI: 10.1021/acs.jcim.2c00982] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Fragment-based drug discovery is a widely used strategy for drug design in both academic and pharmaceutical industries. Although fragments can be linked to generate candidate compounds by the latest deep generative models, generating linkers with specified attributes remains underdeveloped. In this study, we presented a novel framework, DRlinker, to control fragment linking toward compounds with given attributes through reinforcement learning. The method has been shown to be effective for many tasks from controlling the linker length and log P, optimizing predicted bioactivity of compounds, to various multiobjective tasks. Specifically, our model successfully generated 91.0% and 93.9% of compounds complying with the desired linker length and log P and improved the 7.5 pChEMBL value in bioactivity optimization. Finally, a quasi-scaffold-hopping study revealed that DRlinker could generate nearly 30% molecules with high 3D similarity but low 2D similarity to the lead inhibitor, demonstrating the benefits and applicability of DRlinker in actual fragment-based drug design.
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Affiliation(s)
- Youhai Tan
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou510006, China
| | - Lingxue Dai
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou510006, China
| | - Weifeng Huang
- School of Pharmaceutical Science, Sun Yat-sen University, Guangzhou510006, China
| | - Yinfeng Guo
- School of Pharmaceutical Science, Sun Yat-sen University, Guangzhou510006, China
| | - Shuangjia Zheng
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou510006, China.,Galixir Technologies, Beijing100083, China
| | - Jinping Lei
- School of Pharmaceutical Science, Sun Yat-sen University, Guangzhou510006, China
| | - Hongming Chen
- Guangzhou Laboratory, No. 9 XinDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou510005, China
| | - Yuedong Yang
- School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou510006, China
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21
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Urbina F, Ekins S. The Commoditization of AI for Molecule Design. ARTIFICIAL INTELLIGENCE IN THE LIFE SCIENCES 2022; 2:100031. [PMID: 36211981 PMCID: PMC9541920 DOI: 10.1016/j.ailsci.2022.100031] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
Anyone involved in designing or finding molecules in the life sciences over the past few years has witnessed a dramatic change in how we now work due to the COVID-19 pandemic. Computational technologies like artificial intelligence (AI) seemed to become ubiquitous in 2020 and have been increasingly applied as scientists worked from home and were separated from the laboratory and their colleagues. This shift may be more permanent as the future of molecule design across different industries will increasingly require machine learning models for design and optimization of molecules as they become "designed by AI". AI and machine learning has essentially become a commodity within the pharmaceutical industry. This perspective will briefly describe our personal opinions of how machine learning has evolved and is being applied to model different molecule properties that crosses industries in their utility and ultimately suggests the potential for tight integration of AI into equipment and automated experimental pipelines. It will also describe how many groups have implemented generative models covering different architectures, for de novo design of molecules. We also highlight some of the companies at the forefront of using AI to demonstrate how machine learning has impacted and influenced our work. Finally, we will peer into the future and suggest some of the areas that represent the most interesting technologies that may shape the future of molecule design, highlighting how we can help increase the efficiency of the design-make-test cycle which is currently a major focus across industries.
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Affiliation(s)
- Fabio Urbina
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
| | - Sean Ekins
- Collaborations Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, NC 27606, USA
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22
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Gambacorta N, Özdemir Z, Doğan İS, Ciriaco F, Zenni YN, Karakurt A, Saraç S, Nicolotti O. Integrated experimental and theoretical approaches to investigate the molecular mechanisms of the enantioseparation of chiral anticonvulsant and antifungal compounds. J Mol Struct 2022. [DOI: 10.1016/j.molstruc.2022.133905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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23
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Amrane M, Oukid S, Ensari T. SMILES generation against Covid-19 with Encoder-Decoder LSTM and PCA's features. J MECH MED BIOL 2022. [DOI: 10.1142/s0219519422500725] [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]
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24
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Urbina F, Lowden CT, Culberson JC, Ekins S. MegaSyn: Integrating Generative Molecular Design, Automated Analog Designer, and Synthetic Viability Prediction. ACS OMEGA 2022; 7:18699-18713. [PMID: 35694522 PMCID: PMC9178760 DOI: 10.1021/acsomega.2c01404] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 05/11/2022] [Indexed: 05/04/2023]
Abstract
Generative machine learning models have become widely adopted in drug discovery and other fields to produce new molecules and explore molecular space, with the goal of discovering novel compounds with optimized properties. These generative models are frequently combined with transfer learning or scoring of the physicochemical properties to steer generative design, yet often, they are not capable of addressing a wide variety of potential problems, as well as converge into similar molecular space when combined with a scoring function for the desired properties. In addition, these generated compounds may not be synthetically feasible, reducing their capabilities and limiting their usefulness in real-world scenarios. Here, we introduce a suite of automated tools called MegaSyn representing three components: a new hill-climb algorithm, which makes use of SMILES-based recurrent neural network (RNN) generative models, analog generation software, and retrosynthetic analysis coupled with fragment analysis to score molecules for their synthetic feasibility. We show that by deconstructing the targeted molecules and focusing on substructures, combined with an ensemble of generative models, MegaSyn generally performs well for the specific tasks of generating new scaffolds as well as targeted analogs, which are likely synthesizable and druglike. We now describe the development, benchmarking, and testing of this suite of tools and propose how they might be used to optimize molecules or prioritize promising lead compounds using these RNN examples provided by multiple test case examples.
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Affiliation(s)
- Fabio Urbina
- Collaborations
Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
| | - Christopher T. Lowden
- Workflow
Informatics Corporation, 9316 Bramden Court, Wake Forest, North Carolina 27587, United States
| | - J. Christopher Culberson
- Workflow
Informatics Corporation, 9316 Bramden Court, Wake Forest, North Carolina 27587, United States
| | - Sean Ekins
- Collaborations
Pharmaceuticals, Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States
- . Phone: 215-687-1320
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25
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A comprehensive review of Artificial Intelligence and Network based approaches to drug repurposing in Covid-19. Biomed Pharmacother 2022; 153:113350. [PMID: 35777222 PMCID: PMC9236981 DOI: 10.1016/j.biopha.2022.113350] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/22/2022] [Accepted: 06/24/2022] [Indexed: 11/26/2022] Open
Abstract
Conventional drug discovery and development is tedious and time-taking process; because of which it has failed to keep the required pace to mitigate threats and cater demands of viral and re-occurring diseases, such as Covid-19. The main reasons of this delay in traditional drug development are: high attrition rates, extensive time requirements, and huge financial investment with significant risk. The effective solution to de novo drug discovery is drug repurposing. Previous studies have shown that the network-based approaches and analysis are versatile platform for repurposing as the network biology is used to model the interactions between variety of biological concepts. Herein, we provide a comprehensive background of machine learning and deep learning in drug repurposing while specifically focusing on the applications of network-based approach to drug repurposing in Covid-19, data sources, and tools used. Furthermore, use of network proximity, network diffusion, and AI on network-based drug repurposing for Covid-19 is well-explained. Finally, limitations of network-based approaches in general and specific to network are stated along with future recommendations for better network-based models.
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26
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PLATO: A Predictive Drug Discovery Web Platform for Efficient Target Fishing and Bioactivity Profiling of Small Molecules. Int J Mol Sci 2022; 23:ijms23095245. [PMID: 35563636 PMCID: PMC9103655 DOI: 10.3390/ijms23095245] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/03/2022] [Accepted: 05/06/2022] [Indexed: 02/05/2023] Open
Abstract
PLATO (Polypharmacology pLATform predictiOn) is an easy-to-use drug discovery web platform, which has been designed with a two-fold objective: to fish putative protein drug targets and to compute bioactivity values of small molecules. Predictions are based on the similarity principle, through a reverse ligand-based screening, based on a collection of 632,119 compounds known to be experimentally active on 6004 protein targets. An efficient backend implementation allows to speed-up the process that returns results for query in less than 20 s. The graphical user interface is intuitive to give practitioners easy input and transparent output, which is available as a standard report in portable document format. PLATO has been validated on thousands of external data, with performances better than those of other parallel approaches. PLATO is available free of charge (http://plato.uniba.it/ accessed on 13 April 2022).
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27
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Rational Discovery of Antiviral Whey Protein-Derived Small Peptides Targeting the SARS-CoV-2 Main Protease. Biomedicines 2022; 10:biomedicines10051067. [PMID: 35625804 PMCID: PMC9139167 DOI: 10.3390/biomedicines10051067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Revised: 04/30/2022] [Accepted: 04/30/2022] [Indexed: 11/17/2022] Open
Abstract
In the present work, and for the first time, three whey protein-derived peptides (IAEK, IPAVF, MHI), endowed with ACE inhibitory activity, were examined for their antiviral activity against the SARS-CoV-2 3C-like protease (3CLpro) and Human Rhinovirus 3C protease (3Cpro) by employing molecular docking. Computational studies showed reliable binding poses within 3CLpro for the three investigated small peptides, considering docking scores as well as the binding free energy values. Validation by in vitro experiments confirmed these results. In particular, IPAVF exhibited the highest inhibitory activity by returning an IC50 equal to 1.21 μM; it was followed by IAEK, which registered an IC50 of 154.40 μM, whereas MHI was less active with an IC50 equal to 2700.62 μM. On the other hand, none of the assayed peptides registered inhibitory activity against 3Cpro. Based on these results, the herein presented small peptides are introduced as promising molecules to be exploited in the development of “target-specific antiviral” agents against SARS-CoV-2.
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28
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Bilodeau C, Jin W, Jaakkola T, Barzilay R, Jensen KF. Generative models for molecular discovery: Recent advances and challenges. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2022. [DOI: 10.1002/wcms.1608] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Affiliation(s)
- Camille Bilodeau
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Wengong Jin
- Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Tommi Jaakkola
- Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Regina Barzilay
- Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge Massachusetts USA
| | - Klavs F. Jensen
- Department of Chemical Engineering Massachusetts Institute of Technology Cambridge Massachusetts USA
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29
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Unsupervised Learning in Drug Design from Self-Organization to Deep Chemistry. Int J Mol Sci 2022; 23:ijms23052797. [PMID: 35269939 PMCID: PMC8910896 DOI: 10.3390/ijms23052797] [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: 02/03/2022] [Revised: 02/27/2022] [Accepted: 02/27/2022] [Indexed: 12/10/2022] Open
Abstract
The availability of computers has brought novel prospects in drug design. Neural networks (NN) were an early tool that cheminformatics tested for converting data into drugs. However, the initial interest faded for almost two decades. The recent success of Deep Learning (DL) has inspired a renaissance of neural networks for their potential application in deep chemistry. DL targets direct data analysis without any human intervention. Although back-propagation NN is the main algorithm in the DL that is currently being used, unsupervised learning can be even more efficient. We review self-organizing maps (SOM) in mapping molecular representations from the 1990s to the current deep chemistry. We discovered the enormous efficiency of SOM not only for features that could be expected by humans, but also for those that are not trivial to human chemists. We reviewed the DL projects in the current literature, especially unsupervised architectures. DL appears to be efficient in pattern recognition (Deep Face) or chess (Deep Blue). However, an efficient deep chemistry is still a matter for the future. This is because the availability of measured property data in chemistry is still limited.
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30
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Trisciuzzi D, Siragusa L, Baroni M, Autiero I, Nicolotti O, Cruciani G. Getting Insights into Structural and Energetic Properties of Reciprocal Peptide-Protein Interactions. J Chem Inf Model 2022; 62:1113-1125. [PMID: 35148095 DOI: 10.1021/acs.jcim.1c01343] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Peptide-protein interactions play a key role for many cellular and metabolic processes involved in the onset of largely spread diseases such as cancer and neurodegenerative pathologies. Despite the progress in the structural characterization of peptide-protein interfaces, the in-depth knowledge of the molecular details behind their interactions is still a daunting task. Here, we present the first comprehensive in silico morphological and energetic study of peptide binding sites by focusing on both peptide and protein standpoints. Starting from the PixelDB database, a nonredundant benchmark collection of high-quality 3D crystallographic structures of peptide-protein complexes, a classification analysis of the most representative categories based on the nature of each cocrystallized peptide has been carried out. Several interpretable geometrical and energetic descriptors have been computed both from peptide and target protein sides in the attempt to unveil physicochemical and structural causative correlations. Finally, we investigated the most frequent peptide-protein residue pairs at the binding interface and made extensive energetic analyses, based on GRID MIFs, with the aim to study the peptide affinity-enhancing interactions to be further exploited in rational drug design strategies.
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Affiliation(s)
- Daniela Trisciuzzi
- Department of Pharmacy, Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", 70125 Bari, Italy.,Molecular Horizon s.r.l., Via Montelino, 30, 06084 Bettona (PG), Italy
| | - Lydia Siragusa
- Molecular Horizon s.r.l., Via Montelino, 30, 06084 Bettona (PG), Italy.,Molecular Discovery Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, Hertfordshire WD6 4PJ, United Kingdom
| | - Massimo Baroni
- Molecular Discovery Ltd., Kinetic Business Centre, Theobald Street, Elstree, Borehamwood, Hertfordshire WD6 4PJ, United Kingdom
| | - Ida Autiero
- Molecular Horizon s.r.l., Via Montelino, 30, 06084 Bettona (PG), Italy.,National Research Council, Institute of Biostructures and Bioimaging, 80138 Naples, Italy
| | - Orazio Nicolotti
- Department of Pharmacy, Pharmaceutical Sciences, Università degli Studi di Bari "Aldo Moro", 70125 Bari, Italy
| | - Gabriele Cruciani
- Department of Chemistry, Biology and Biotechnology, Università degli Studi di Perugia, via Elce di Sotto, 8, 06123 Perugia (PG), Italy
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31
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Iovanac NC, MacKnight R, Savoie BM. Actively Searching: Inverse Design of Novel Molecules with Simultaneously Optimized Properties. J Phys Chem A 2022; 126:333-340. [PMID: 34985908 DOI: 10.1021/acs.jpca.1c08191] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Combining quantum chemistry characterizations with generative machine learning models has the potential to accelerate molecular discovery. In this paradigm, quantum chemistry acts as a relatively cost-effective oracle for evaluating the properties of particular molecules, while generative models provide a means of sampling chemical space based on learned structure-function relationships. For practical applications, multiple potentially orthogonal properties must be optimized in tandem during a discovery workflow. This carries additional difficulties associated with the specificity of the targets and the ability for the model to reconcile all properties simultaneously. Here, we demonstrate an active learning approach to improve the performance of multi-target generative chemical models. We first demonstrate the effectiveness of a set of baseline models trained on single property prediction tasks in generating novel compounds (i.e., not present in the training data) with various property targets, including both interpolative and extrapolative generation scenarios. For property ranges where accurate targeting proves difficult, the novel compounds suggested by the model are characterized using quantum chemistry and the new molecules closest to expressing the desired properties are fed back into the generative model for additional training. This gradually improves the generative models' understanding of targeted areas of chemical space and shifts the distribution of the generated compounds toward the targeted values. We then demonstrate the effectiveness of this active learning approach in generating compounds with multiple chemical constraints, including vertical ionization potential, electron affinity, and dipole moment targets, and validate the results at the ωB97X-D3/def2-TZVP level. This method requires no modifications to extant generative approaches, but rather utilizes their inherent generative and predictive aspects for self-refinement, and can be applied to situations where any number of properties with varying degrees of correlation must be optimized simultaneously.
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Affiliation(s)
- Nicolae C Iovanac
- Charles D. Davidson School of Chemical Engineering, Purdue University, 480 Stadium Mall Drive, West Lafayette, Indiana 47906, United States
| | - Robert MacKnight
- Charles D. Davidson School of Chemical Engineering, Purdue University, 480 Stadium Mall Drive, West Lafayette, Indiana 47906, United States
| | - Brett M Savoie
- Charles D. Davidson School of Chemical Engineering, Purdue University, 480 Stadium Mall Drive, West Lafayette, Indiana 47906, United States
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32
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Bilsland AE, Pugliese A, Bower J. Implementation of an AI-assisted fragment-generator in an open-source platform. RSC Med Chem 2022; 13:1205-1211. [PMID: 36320432 PMCID: PMC9579942 DOI: 10.1039/d2md00152g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 07/27/2022] [Indexed: 11/21/2022] Open
Abstract
We recently reported a deep learning model to facilitate fragment library design, which is critical for efficient hit identification. However, our model was implemented in Python. We have now created an implementation in the KNIME graphical pipelining environment which we hope will allow experimentation by users with limited programming knowledge. We report a deep learning model to facilitate fragment library design, which is critical for efficient hit identification, and an implementation in the KNIME graphical workflow environment which should facilitate a more codeless use.![]()
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Affiliation(s)
- Alan E. Bilsland
- Cancer Research Horizons – Therapeutic Innovation, Cancer Research UK Beatson Institute, Garscube Estate, Switchback Road, Glasgow G61 1BD, UK
| | - Angelo Pugliese
- BioAscent Discovery, Bo'Ness Road, Newhouse, Lanarkshire ML1 5UH, UK
| | - Justin Bower
- Cancer Research Horizons – Therapeutic Innovation, Cancer Research UK Beatson Institute, Garscube Estate, Switchback Road, Glasgow G61 1BD, UK
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33
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Deng J, Yang Z, Ojima I, Samaras D, Wang F. Artificial intelligence in drug discovery: applications and techniques. Brief Bioinform 2021; 23:6420092. [PMID: 34734228 DOI: 10.1093/bib/bbab430] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Revised: 08/02/2021] [Accepted: 09/18/2021] [Indexed: 12/23/2022] Open
Abstract
Artificial intelligence (AI) has been transforming the practice of drug discovery in the past decade. Various AI techniques have been used in many drug discovery applications, such as virtual screening and drug design. In this survey, we first give an overview on drug discovery and discuss related applications, which can be reduced to two major tasks, i.e. molecular property prediction and molecule generation. We then present common data resources, molecule representations and benchmark platforms. As a major part of the survey, AI techniques are dissected into model architectures and learning paradigms. To reflect the technical development of AI in drug discovery over the years, the surveyed works are organized chronologically. We expect that this survey provides a comprehensive review on AI in drug discovery. We also provide a GitHub repository with a collection of papers (and codes, if applicable) as a learning resource, which is regularly updated.
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Affiliation(s)
- Jianyuan Deng
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA
| | - Zhibo Yang
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11790, USA
| | - Iwao Ojima
- Department of Chemistry, Stony Brook University, Stony Brook, NY 11790, USA
| | - Dimitris Samaras
- Department of Computer Science, Stony Brook University, Stony Brook, NY 11790, USA
| | - Fusheng Wang
- Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY 11790, USA.,Department of Computer Science, Stony Brook University, Stony Brook, NY 11790, USA
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34
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Choi J, Lee J. V-Dock: Fast Generation of Novel Drug-like Molecules Using Machine-Learning-Based Docking Score and Molecular Optimization. Int J Mol Sci 2021; 22:11635. [PMID: 34769065 PMCID: PMC8584000 DOI: 10.3390/ijms222111635] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/13/2021] [Accepted: 10/24/2021] [Indexed: 02/06/2023] Open
Abstract
We propose a computational workflow to design novel drug-like molecules by combining the global optimization of molecular properties and protein-ligand docking with machine learning. However, most existing methods depend heavily on experimental data, and many targets do not have sufficient data to train reliable activity prediction models. To overcome this limitation, protein-ligand docking calculations must be performed using the limited data available. Such docking calculations during molecular generation require considerable computational time, preventing extensive exploration of the chemical space. To address this problem, we trained a machine-learning-based model that predicted the docking energy using SMILES to accelerate the molecular generation process. Docking scores could be accurately predicted using only a SMILES string. We combined this docking score prediction model with the global molecular property optimization approach, MolFinder, to find novel molecules exhibiting the desired properties with high values of predicted docking scores. We named this design approach V-dock. Using V-dock, we efficiently generated many novel molecules with high docking scores for a target protein, a similarity to the reference molecule, and desirable drug-like and bespoke properties, such as QED. The predicted docking scores of the generated molecules were verified by correlating them with the actual docking scores.
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Affiliation(s)
- Jieun Choi
- Department of Chemistry, Division of Chemistry and Biochemistry, Kangwon National University, Chuncheon 24341, Korea;
| | - Juyong Lee
- Department of Chemistry, Division of Chemistry and Biochemistry, Kangwon National University, Chuncheon 24341, Korea;
- Arontier Co., Seoul 06735, Korea
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35
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Ciriaco F, Gambacorta N, Alberga D, Nicolotti O. Quantitative Polypharmacology Profiling Based on a Multifingerprint Similarity Predictive Approach. J Chem Inf Model 2021; 61:4868-4876. [PMID: 34570498 DOI: 10.1021/acs.jcim.1c00498] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
We present a new quantitative ligand-based bioactivity prediction approach employing a multifingerprint similarity search algorithm, enabling the polypharmacological profiling of small molecules. Quantitative bioactivity predictions are made on the basis of the statistical distributions of multiple Tanimoto similarity θ values, calculated through 13 different molecular fingerprints, and of the variation of the measured biological activity, reported as ΔpIC50, for all of the ligands sharing a given protein drug target. The application data set comprises as much as 4241 protein drug targets as well as 418 485 ligands selected from ChEMBL (release 25) by employing a set of well-defined filtering rules. Several large internal and external validation studies were carried out to demonstrate the robustness and the predictive potential of the herein proposed method. Additional comparative studies, carried out on two freely available and well-known ligand-target prediction platforms, demonstrated the reliability of our proposed approach for accurate ligand-target matching. Moreover, two applicative cases were also discussed to practically describe how to use our predictive algorithm, which is freely available as a user-friendly web platform. The user can screen single or multiple queries at a time and retrieve the output as a terse html table or as a json file including all of the information concerning the explored similarities to obtain a deeper understanding of the results. High-throughput virtual reverse screening campaigns, allowing for a given query compound the quick detection of the potential drug target from a large collection of them, can be carried out in batch on demand.
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Affiliation(s)
- Fulvio Ciriaco
- Dipartimento di Chimica, Università degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, I-70126 Bari, Italy
| | - Nicola Gambacorta
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, I-70126 Bari, Italy
| | - Domenico Alberga
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, I-70126 Bari, Italy
| | - Orazio Nicolotti
- Dipartimento di Farmacia-Scienze del Farmaco, Università degli Studi di Bari "Aldo Moro", Via E. Orabona, 4, I-70126 Bari, Italy
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36
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Abstract
This review provides the feasible literature on drug discovery through ML tools and techniques that are enforced in every phase of drug development to accelerate the research process and deduce the risk and expenditure in clinical trials. Machine learning techniques improve the decision-making in pharmaceutical data across various applications like QSAR analysis, hit discoveries, de novo drug architectures to retrieve accurate outcomes. Target validation, prognostic biomarkers, digital pathology are considered under problem statements in this review. ML challenges must be applicable for the main cause of inadequacy in interpretability outcomes that may restrict the applications in drug discovery. In clinical trials, absolute and methodological data must be generated to tackle many puzzles in validating ML techniques, improving decision-making, promoting awareness in ML approaches, and deducing risk failures in drug discovery.
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Affiliation(s)
- Suresh Dara
- Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Medak, 502313 Telangana India
| | - Swetha Dhamercherla
- Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Medak, 502313 Telangana India
| | - Surender Singh Jadav
- Centre for Molecular Cancer Research (CMCR) and Vishnu Institute of Pharmaceutical Education and Research (VIPER), Narsapur, Medak, 502313 Telangana India
| | - CH Madhu Babu
- Department of Computer Science and Engineering, B V Raju Institute of Technology, Narsapur, Medak, 502313 Telangana India
| | - Mohamed Jawed Ahsan
- Department of Pharmaceutical Chemistry, Maharishi Arvind College of Pharmacy, Jaipur, 302023 Rajasthan India
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37
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Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021; 25:1315-1360. [PMID: 33844136 PMCID: PMC8040371 DOI: 10.1007/s11030-021-10217-3] [Citation(s) in RCA: 253] [Impact Index Per Article: 84.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Accepted: 03/22/2021] [Indexed: 02/06/2023]
Abstract
Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure-activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure-activity relationship to drug repositioning, protein misfolding to protein-protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screening, biomarker development, pharmaceutical manufacturing, bioactivity identification and physiochemical properties, prediction of toxicity, and identification of mode of action.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Devesh Srivastava
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Mehar Sahu
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Swati Tiwari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological University (Formerly DCE), Shahbad Daulatpur, Bawana Road, Delhi, 110042, India.
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38
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Rácz A, Bajusz D, Miranda-Quintana RA, Héberger K. Machine learning models for classification tasks related to drug safety. Mol Divers 2021; 25:1409-1424. [PMID: 34110577 PMCID: PMC8342376 DOI: 10.1007/s11030-021-10239-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Accepted: 05/27/2021] [Indexed: 12/23/2022]
Abstract
In this review, we outline the current trends in the field of machine learning-driven classification studies related to ADME (absorption, distribution, metabolism and excretion) and toxicity endpoints from the past six years (2015-2021). The study focuses only on classification models with large datasets (i.e. more than a thousand compounds). A comprehensive literature search and meta-analysis was carried out for nine different targets: hERG-mediated cardiotoxicity, blood-brain barrier penetration, permeability glycoprotein (P-gp) substrate/inhibitor, cytochrome P450 enzyme family, acute oral toxicity, mutagenicity, carcinogenicity, respiratory toxicity and irritation/corrosion. The comparison of the best classification models was targeted to reveal the differences between machine learning algorithms and modeling types, endpoint-specific performances, dataset sizes and the different validation protocols. Based on the evaluation of the data, we can say that tree-based algorithms are (still) dominating the field, with consensus modeling being an increasing trend in drug safety predictions. Although one can already find classification models with great performances to hERG-mediated cardiotoxicity and the isoenzymes of the cytochrome P450 enzyme family, these targets are still central to ADMET-related research efforts.
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Affiliation(s)
- Anita Rácz
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, Budapest, 1117, Hungary.
| | - Dávid Bajusz
- Medicinal Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, Budapest, 1117, Hungary
| | | | - Károly Héberger
- Plasma Chemistry Research Group, Research Centre for Natural Sciences, Magyar tudósok krt. 2, Budapest, 1117, Hungary.
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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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Vázquez J, López M, Gibert E, Herrero E, Luque FJ. Merging Ligand-Based and Structure-Based Methods in Drug Discovery: An Overview of Combined Virtual Screening Approaches. Molecules 2020; 25:E4723. [PMID: 33076254 PMCID: PMC7587536 DOI: 10.3390/molecules25204723] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 10/06/2020] [Accepted: 10/11/2020] [Indexed: 12/20/2022] Open
Abstract
Virtual screening (VS) is an outstanding cornerstone in the drug discovery pipeline. A variety of computational approaches, which are generally classified as ligand-based (LB) and structure-based (SB) techniques, exploit key structural and physicochemical properties of ligands and targets to enable the screening of virtual libraries in the search of active compounds. Though LB and SB methods have found widespread application in the discovery of novel drug-like candidates, their complementary natures have stimulated continued efforts toward the development of hybrid strategies that combine LB and SB techniques, integrating them in a holistic computational framework that exploits the available information of both ligand and target to enhance the success of drug discovery projects. In this review, we analyze the main strategies and concepts that have emerged in the last years for defining hybrid LB + SB computational schemes in VS studies. Particularly, attention is focused on the combination of molecular similarity and docking, illustrating them with selected applications taken from the literature.
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Affiliation(s)
- Javier Vázquez
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB), University of Barcelona, Av. Prat de la Riba 171, E-08921 Santa Coloma de Gramanet, Spain
| | - Manel López
- AB Science, Parc Scientifique de Luminy, Zone Luminy Enterprise, Case 922, 163 Av. de Luminy, 13288 Marseille, France;
| | - Enric Gibert
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
| | - Enric Herrero
- Pharmacelera, Plaça Pau Vila, 1, Sector C 2a, Edificio Palau de Mar, 08039 Barcelona, Spain;
| | - F. Javier Luque
- Department of Nutrition, Food Science and Gastronomy, Faculty of Pharmacy and Food Sciences, Institute of Biomedicine (IBUB), and Institute of Theoretical and Computational Chemistry (IQTC-UB), University of Barcelona, Av. Prat de la Riba 171, E-08921 Santa Coloma de Gramanet, Spain
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Carofiglio F, Trisciuzzi D, Gambacorta N, Leonetti F, Stefanachi A, Nicolotti O. Bcr-Abl Allosteric Inhibitors: Where We Are and Where We Are Going to. Molecules 2020; 25:E4210. [PMID: 32937901 PMCID: PMC7570842 DOI: 10.3390/molecules25184210] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 09/08/2020] [Accepted: 09/10/2020] [Indexed: 12/12/2022] Open
Abstract
The fusion oncoprotein Bcr-Abl is an aberrant tyrosine kinase responsible for chronic myeloid leukemia and acute lymphoblastic leukemia. The auto-inhibition regulatory module observed in the progenitor kinase c-Abl is lost in the aberrant Bcr-Abl, because of the lack of the N-myristoylated cap able to bind the myristoyl binding pocket also conserved in the Bcr-Abl kinase domain. A way to overcome the occurrence of resistance phenomena frequently observed for Bcr-Abl orthosteric drugs is the rational design of allosteric ligands approaching the so-called myristoyl binding pocket. The discovery of these allosteric inhibitors although very difficult and extremely challenging, represents a valuable option to minimize drug resistance, mostly due to the occurrence of mutations more frequently affecting orthosteric pockets, and to enhance target selectivity with lower off-target effects. In this perspective, we will elucidate at a molecular level the structural bases behind the Bcr-Abl allosteric control and will show how artificial intelligence can be effective to drive the automated de novo design towards off-patent regions of the chemical space.
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Affiliation(s)
- Francesca Carofiglio
- Dipartimento di Farmacia Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, 70125 Bari, Italy; (F.C.); (D.T.); (N.G.); (F.L.)
| | - Daniela Trisciuzzi
- Dipartimento di Farmacia Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, 70125 Bari, Italy; (F.C.); (D.T.); (N.G.); (F.L.)
- Molecular Horizon srl, Via Montelino 32, 06084 Bettona, Italy
| | - Nicola Gambacorta
- Dipartimento di Farmacia Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, 70125 Bari, Italy; (F.C.); (D.T.); (N.G.); (F.L.)
| | - Francesco Leonetti
- Dipartimento di Farmacia Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, 70125 Bari, Italy; (F.C.); (D.T.); (N.G.); (F.L.)
| | - Angela Stefanachi
- Dipartimento di Farmacia Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, 70125 Bari, Italy; (F.C.); (D.T.); (N.G.); (F.L.)
| | - Orazio Nicolotti
- Dipartimento di Farmacia Scienze del Farmaco, Università degli Studi di Bari “Aldo Moro”, 70125 Bari, Italy; (F.C.); (D.T.); (N.G.); (F.L.)
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