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Li G, Li J, Tian Y, Zhao Y, Pang X, Yan A. Machine learning-based classification models for non-covalent Bruton's tyrosine kinase inhibitors: predictive ability and interpretability. Mol Divers 2023:10.1007/s11030-023-10696-6. [PMID: 37479824 DOI: 10.1007/s11030-023-10696-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2023] [Accepted: 07/07/2023] [Indexed: 07/23/2023]
Abstract
In this study, we built classification models using machine learning techniques to predict the bioactivity of non-covalent inhibitors of Bruton's tyrosine kinase (BTK) and to provide interpretable and transparent explanations for these predictions. To achieve this, we gathered data on BTK inhibitors from the Reaxys and ChEMBL databases, removing compounds with covalent bonds and duplicates to obtain a dataset of 3895 inhibitors of non-covalent. These inhibitors were characterized using MACCS fingerprints and Morgan fingerprints, and four traditional machine learning algorithms (decision trees (DT), random forests (RF), support vector machines (SVM), and extreme gradient boosting (XGBoost)) were used to build 16 classification models. In addition, four deep learning models were developed using deep neural networks (DNN). The best model, Model D_4, which was built using XGBoost and MACCS fingerprints, achieved an accuracy of 94.1% and a Matthews correlation coefficient (MCC) of 0.75 on the test set. To provide interpretable explanations, we employed the SHAP method to decompose the predicted values into the contributions of each feature. We also used K-means dimensionality reduction and hierarchical clustering to visualize the clustering effects of molecular structures of the inhibitors. The results of this study were validated using crystal structures, and we found that the interaction between the BTK amino acid residue and the important features of clustered scaffold was consistent with the known properties of the complex crystal structures. Overall, our models demonstrated high predictive ability and a qualitative model can be converted to a quantitative model to some extent by SHAP, making them valuable for guiding the design of new BTK inhibitors with desired activity.
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Affiliation(s)
- Guo Li
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, People's Republic of China
| | - Jiaxuan Li
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, People's Republic of China
| | - Yujia Tian
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, People's Republic of China
| | - Yunyang Zhao
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, People's Republic of China
| | - Xiaoyang Pang
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, People's Republic of China
| | - Aixia Yan
- State Key Laboratory of Chemical Resource Engineering, Department of Pharmaceutical Engineering, Beijing University of Chemical Technology, Beijing, People's Republic of China.
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2
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Konen FF, Möhn N, Witte T, Schefzyk M, Wiestler M, Lovric S, Hufendiek K, Schwenkenbecher P, Sühs KW, Friese MA, Klotz L, Pul R, Pawlitzki M, Hagin D, Kleinschnitz C, Meuth SG, Skripuletz T. Treatment of autoimmunity: The impact of disease-modifying therapies in multiple sclerosis and comorbid autoimmune disorders. Autoimmun Rev 2023; 22:103312. [PMID: 36924922 DOI: 10.1016/j.autrev.2023.103312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 03/09/2023] [Indexed: 03/17/2023]
Abstract
More than 10 disease-modifying therapies (DMT) are approved by the European Medicines Agency (EMA) and the US Food and Drug Administration (FDA) for the treatment of multiple sclerosis (MS) and new therapeutic options are on the horizon. Due to different underlying therapeutic mechanisms, a more individualized selection of DMTs in MS is possible, taking into account the patient's current situation. Therefore, concomitant treatment of various comorbid conditions, including autoimmune mediated disorders such as rheumatoid arthritis, should be considered in MS patients. Because the pathomechanisms of autoimmunity partially overlap, DMT could also treat concomitant inflammatory diseases and simplify the patient's treatment. In contrast, the exacerbation and even new occurrence of several autoimmune diseases have been reported as a result of immunomodulatory treatment of MS. To simplify treatment and avoid disease exacerbation, knowledge of the beneficial and adverse effects of DMT in other autoimmune disorders is critical. Therefore, we conducted a literature search and described the beneficial and adverse effects of approved and currently studied DMT in a large number of comorbid autoimmune diseases, including rheumatoid arthritis, ankylosing spondylitis, inflammatory bowel diseases, cutaneous disorders including psoriasis, Sjögren´s syndrome, systemic lupus erythematosus, systemic vasculitis, autoimmune hepatitis, and ocular autoimmune disorders. Our review aims to facilitate the selection of an appropriate DMT in patients with MS and comorbid autoimmune diseases.
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Affiliation(s)
- Franz Felix Konen
- Department of Neurology, Hannover Medical School, 30625 Hannover, Germany..
| | - Nora Möhn
- Department of Neurology, Hannover Medical School, 30625 Hannover, Germany..
| | - Torsten Witte
- Department of Rheumatology and Clinical Immunology, Hannover Medical School, 30625 Hannover, Germany..
| | - Matthias Schefzyk
- Department of Dermatology, Allergology and Venerology, Hannover Medical School, 30625 Hannover, Germany..
| | - Miriam Wiestler
- Department of Internal Medicine, Division of Gastroenterology, Hepatology and Endocrinology, Hannover Medical School, 30625 Hannover, Germany.
| | - Svjetlana Lovric
- Department of Nephrology and Hypertension, Hannover Medical School, 30625 Hannover, Germany.
| | - Karsten Hufendiek
- University Eye Hospital, Hannover Medical School, 30625 Hannover, Germany.
| | | | - Kurt-Wolfram Sühs
- Department of Neurology, Hannover Medical School, 30625 Hannover, Germany..
| | - Manuel A Friese
- Institute of Neuroimmunology and Multiple Sclerosis, Center for Molecular Neurobiology Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg 20251, Germany.
| | - Luisa Klotz
- Department of Neurology with Institute of Translational Neurology, University Hospital Muenster, 48149 Muenster, Germany.
| | - Refik Pul
- Department of Neurology, University Medicine Essen, Essen, Germany; Center for Translational Neuro- and Behavioral Sciences, University Hospital Essen, Essen 45147, Germany.
| | - Marc Pawlitzki
- Department of Neurology, Medical Faculty, Heinrich Heine University Dusseldorf, 40225 Dusseldorf, Germany.
| | - David Hagin
- Allergy and Clinical Immunology Unit, Department of Medicine, Tel-Aviv Sourasky Medical Center and Sackler Faculty of Medicine, University of Tel Aviv, 6 Weizmann St., Tel-Aviv 6423906, Israel.
| | - Christoph Kleinschnitz
- Department of Neurology, University Medicine Essen, Essen, Germany; Center for Translational Neuro- and Behavioral Sciences, University Hospital Essen, Essen 45147, Germany.
| | - Sven G Meuth
- Department of Neurology, Medical Faculty, Heinrich Heine University Dusseldorf, 40225 Dusseldorf, Germany.
| | - Thomas Skripuletz
- Department of Neurology, Hannover Medical School, 30625 Hannover, Germany..
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3
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Palermo A. Metabolomics- and systems-biology-guided discovery of metabolite lead compounds and druggable targets. Drug Discov Today 2023; 28:103460. [PMID: 36427778 DOI: 10.1016/j.drudis.2022.103460] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 11/16/2022] [Accepted: 11/21/2022] [Indexed: 11/27/2022]
Abstract
Metabolomics enables the comprehensive and unbiased analysis of metabolites and lipids in biological systems. In conjunction with high-throughput activity screening, big data and synthetic biology, metabolomics can guide the discovery of lead compounds with pharmacological activity from natural sources and the gut microbiome. In combination with other omics, metabolomics can further unlock the elucidation of compound toxicity, the mode of action and novel druggable targets of disease. Here, we discuss the workflows, limitations and future opportunities to leverage metabolomics and big data in conjunction with systems and synthetic biology for streamlining the discovery and development of molecules of pharmaceutical interest.
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Affiliation(s)
- Amelia Palermo
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, UCLA, Los Angeles, CA 90095, USA.
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4
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Liu K, Chen X, Ren Y, Liu C, Lv T, Liu Y, Zhang Y. Multi-target-based polypharmacology prediction (mTPP): An approach using virtual screening and machine learning for multi-target drug discovery. Chem Biol Interact 2022. [DOI: 10.1016/j.cbi.2022.110239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Revised: 09/19/2022] [Accepted: 10/21/2022] [Indexed: 11/23/2022]
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Raju B, Narendra G, Verma H, Kumar M, Sapra B, Kaur G, jain SK, Silakari O. Machine Learning Enabled Structure-Based Drug Repurposing Approach to Identify Potential CYP1B1 Inhibitors. ACS Omega 2022; 7:31999-32013. [PMID: 36120033 PMCID: PMC9476183 DOI: 10.1021/acsomega.2c02983] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 08/23/2022] [Indexed: 06/15/2023]
Abstract
Drug-metabolizing enzyme (DME)-mediated pharmacokinetic resistance of some clinically approved anticancer agents is one of the main reasons for cancer treatment failure. In particular, some commonly used anticancer medicines, including docetaxel, tamoxifen, imatinib, cisplatin, and paclitaxel, are inactivated by CYP1B1. Currently, no approved drugs are available to treat this CYP1B1-mediated inactivation, making the pharmaceutical industries strive to discover new anticancer agents. Because of the extreme complexity and high risk in drug discovery and development, it is worthwhile to come up with a drug repurposing strategy that may solve the resistance problem of existing chemotherapeutics. Therefore, in the current study, a drug repurposing strategy was implemented to find the possible CYP1B1 inhibitors using machine learning (ML) and structure-based virtual screening (SB-VS) approaches. Initially, three different ML models were developed such as support vector machines (SVMs), random forest (RF), and artificial neural network (ANN); subsequently, the best-selected ML model was employed for virtual screening of the selleckchem database to identify potential CYP1B1 inhibitors. The inhibition potency of the obtained hits was judged by analyzing the crucial active site amino acid interactions against CYP1B1. After a thorough assessment of docking scores, binding affinities, as well as binding modes, four compounds were selected and further subjected to in vitro analysis. From the in vitro analysis, it was observed that chlorprothixene, nadifloxacin, and ticagrelor showed promising inhibitory activity toward CYP1B1 in the IC50 range of 0.07-3.00 μM. These new chemical scaffolds can be explored as adjuvant therapies to address CYP1B1-mediated drug-resistance problems.
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Affiliation(s)
- Baddipadige Raju
- Molecular
Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug
Research, Punjabi University, Patiala, Punjab 147002, India
| | - Gera Narendra
- Molecular
Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug
Research, Punjabi University, Patiala, Punjab 147002, India
| | - Himanshu Verma
- Molecular
Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug
Research, Punjabi University, Patiala, Punjab 147002, India
| | - Manoj Kumar
- Molecular
Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug
Research, Punjabi University, Patiala, Punjab 147002, India
| | - Bharti Sapra
- Molecular
Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug
Research, Punjabi University, Patiala, Punjab 147002, India
| | - Gurleen Kaur
- Center
for Basic and Translational Research in Health Sciences, Guru Nanak Dev University, Amritsar 143005, India
| | - Subheet Kumar jain
- Center
for Basic and Translational Research in Health Sciences, Guru Nanak Dev University, Amritsar 143005, India
| | - Om Silakari
- Molecular
Modeling Lab (MML), Department of Pharmaceutical Sciences and Drug
Research, Punjabi University, Patiala, Punjab 147002, India
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Shanmugam S, Sugumaran V, Thangavelu A, Sekaran K. Predicting rheumatoid arthritis from the biomarkers of clinical trials using improved harmony search optimization with adaptive neuro-fuzzy inference system. IFS 2022. [DOI: 10.3233/jifs-221252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Rheumatoid Arthritis (RA) is a chronic autoimmune disease whose symptoms are hard to determine due to the overlapping indications of the condition with other illnesses such as dengue, malaria, etc. As the symptoms of RA disease are similar to inflammatory diseases, general physicians (GPs) find it difficult to detect the disease earlier. A computer aided framework is proposed in this study to assist and support the GPs to diagnose RA better. In this work Improved Harmony Search Optimization (IHSO) approach is proposed to select the significant feature subset of RA and Adaptive Neuro-Fuzzy Inference System (ANFIS) is used as a classification model. The performance of the proposed IHSO-ANFIS model is examined with metrics such as Balanced Accuracy (Bacc), Area under Curve (AUC), Sensitivity (Sen), Specificity (Spec), and Matthew’s Correlation Coefficient (MCC) using 10-Fold cross-validation. Additionally, the results of the IHSO-ANFIS are compared with HSO-ANFIS, ANFIS without any feature selection and standard bench mark datasets. IHSO-ANFIS attained 87.05% Bacc, 89.95% AUC and 0.6586 MCC on the RA dataset. From the results it is clear that IHSO-ANFIS could assist general physicians to diagnose RA earlier and pave the way for timely treatment.
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Affiliation(s)
- S. Shanmugam
- Department of Computing Technology, SRM Institute of Science and Technology, Chennai, Tamil Nadu, INDIA
| | - Vijayan Sugumaran
- Department of Decision and Information Sciences, School of Business Administration and Centre for Data Science and Big Data Analytics, Oakland University, Rochester, Michigan, USA
| | | | - Karthik Sekaran
- School of Bio Science and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu, INDIA
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Alqahtani A. Application of Artificial Intelligence in Discovery and Development of Anticancer and Antidiabetic Therapeutic Agents. Evid Based Complement Alternat Med 2022; 2022:6201067. [PMID: 35509623 PMCID: PMC9060979 DOI: 10.1155/2022/6201067] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 03/17/2022] [Accepted: 04/05/2022] [Indexed: 11/18/2022]
Abstract
Spectacular developments in molecular and cellular biology have led to important discoveries in cancer research. Despite cancer is one of the major causes of morbidity and mortality globally, diabetes is one of the most leading sources of group of disorders. Artificial intelligence (AI) has been considered the fourth industrial revolution machine. The most major hurdles in drug discovery and development are the time and expenditures required to sustain the drug research pipeline. Large amounts of data can be explored and generated by AI, which can then be converted into useful knowledge. Because of this, the world's largest drug companies have already begun to use AI in their drug development research. In the present era, AI has a huge amount of potential for the rapid discovery and development of new anticancer drugs. Clinical studies, electronic medical records, high-resolution medical imaging, and genomic assessments are just a few of the tools that could aid drug development. Large data sets are available to researchers in the pharmaceutical and medical fields, which can be analyzed by advanced AI systems. This review looked at how computational biology and AI technologies may be utilized in cancer precision drug development by combining knowledge of cancer medicines, drug resistance, and structural biology. This review also highlighted a realistic assessment of the potential for AI in understanding and managing diabetes.
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Affiliation(s)
- Amal Alqahtani
- College of Medicine, Imam Abdulrahman Bin Faisal University, Dammam, 31541, Saudi Arabia
- Department of Basic Sciences, Deanship of Preparatory Year and Supporting Studies, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 34212, Saudi Arabia
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8
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Kumar SA, Ananda Kumar TD, Beeraka NM, Pujar GV, Singh M, Narayana Akshatha HS, Bhagyalalitha M. Machine learning & deep learning in data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry. Future Med Chem 2021. [PMID: 34939433 DOI: 10.4155/fmc-2021-0243] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) have substantial implications in virtual screening, peptide synthesis, drug ADMET screening and biomarker discovery. These strategies can increase the positive outcomes in the drug discovery process without false-positive rates and can be achieved in a cost-effective way with a minimum duration of time by high-quality data acquisition. This review substantially discusses the recent updates in AI tools as cheminformatics application in medicinal chemistry for the data-driven decision making of drug discovery and challenges in high-quality data acquisition in the pharmaceutical industry while improving small-molecule bioactivities and properties.
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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: 228] [Impact Index Per Article: 76.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Tripathi MK, Nath A, Singh TP, Ethayathulla AS, Kaur P. Evolving scenario of big data and Artificial Intelligence (AI) in drug discovery. Mol Divers 2021; 25:1439-1460. [PMID: 34159484 PMCID: PMC8219515 DOI: 10.1007/s11030-021-10256-w] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Accepted: 06/14/2021] [Indexed: 12/24/2022]
Abstract
The accumulation of massive data in the plethora of Cheminformatics databases has made the role of big data and artificial intelligence (AI) indispensable in drug design. This has necessitated the development of newer algorithms and architectures to mine these databases and fulfil the specific needs of various drug discovery processes such as virtual drug screening, de novo molecule design and discovery in this big data era. The development of deep learning neural networks and their variants with the corresponding increase in chemical data has resulted in a paradigm shift in information mining pertaining to the chemical space. The present review summarizes the role of big data and AI techniques currently being implemented to satisfy the ever-increasing research demands in drug discovery pipelines.
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Affiliation(s)
- Manish Kumar Tripathi
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, 110029, India
| | - Abhigyan Nath
- Department of Biochemistry, Pt. Jawahar Lal Nehru Memorial Medical College, Raipur, 492001, India
| | - Tej P Singh
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, 110029, India
| | - A S Ethayathulla
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, 110029, India
| | - Punit Kaur
- Department of Biophysics, All India Institute of Medical Sciences, New Delhi, 110029, India.
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