Application of Artificial Intelligence and Machine Learning in Drug Discovery

2021 ◽  
pp. 113-124
Author(s):  
Rishi R. Gupta
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Discovery

Clinical Trials and Machine Learning: Regulatory Approach Review

2021 ◽  
Vol 16 ◽  
Author(s):  
Diego Alejandro Dri ◽  
Maurizio Massella ◽  
Donatella Gramaglia ◽  
Carlotta Marianecci ◽  
Sandra Petraglia
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Trials ◽  
Patient Safety ◽  
Drug Discovery ◽  
Clinical Applications ◽  
Clinical Development ◽  
Regulatory Approach ◽  
National Competent Authorities ◽  
Review Current

: Machine Learning, a fast-growing technology, is an application of Artificial Intelligence that has significantly contributed to drug discovery and clinical development. In the last few years, the number of clinical applications based on Machine Learning has constantly been growing. Moreover, it is now also impacting National Competent Authorities during the assessment of most recently submitted Clinical Trials that are designed, managed, or generating data deriving from the use of Machine Learning or Artificial Intelligence technologies. We review current information available on the regulatory approach to Clinical Trials and Machine Learning. We also provide inputs for further reasoning and potential indications, including six actionable proposals for regulators to proactively drive the upcoming evolution of Clinical Trials within a strong regulatory framework, focusing on patient safety, health protection, and fostering immediate access to effective treatments.

Artificial Intelligence Recognizes β-Lapachone as an Allosteric 5- Lipoxygenase Inhibitor

2018 ◽  
Author(s):  
Gonçalo Bernardes ◽  
Tiago Rodrigues ◽  
Markus Werner ◽  
Jakob Roth ◽  
Eduardo H. G. da Cruz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Discovery ◽  
Natural Product ◽  
Systems Pharmacology ◽  
Lipoxygenase Inhibitor ◽  
Machine Learning Methods ◽  
Biochemical Assays ◽  
Viable Approach ◽  
Chemical Matter

<div> <div> <div> <p>Chemical matter with often-discarded moieties entails opportunities for drug discovery. Relying on orthogonal ligand-centric machine learning methods, targets were consensually identified as potential counterparts for the fragment-like natural product β-lapachone. Resorting to a comprehensive range of biophysical and biochemical assays, the natural product was validated as a potent, ligand efficient, allosteric and reversible modulator of 5-lipoxygenase (5-LO). Moreover, we provide a rationale for 5-LO-inhibiting chemotypes inspired in the β-lapachone scaffold through a focused analogue library. This work demonstrates the power of artificial intelligence technologies to deconvolute complex phenotypic readouts of clinically relevant chemical matter, leverage natural product-based drug discovery, as an alternative and/or complement to chemoproteomics and as a viable approach for systems pharmacology studies. </p> </div> </div> </div>

scholarly journals Transformation of Drug Discovery towards Artificial Intelligence: An in Silico Approach

2021 ◽  
Author(s):  
Ruby Srivastava
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Discovery ◽  
In Silico ◽  
Drug Repurposing ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Modern Drug ◽  
Short Period

Computational methods play a key role in the design of therapeutically important molecules for modern drug development. With these “in silico” approaches, machines are learning and offering solutions to some of the most complex drug related problems and has well positioned them as a next frontier for potential breakthrough in drug discovery. Machine learning (ML) methods are used to predict compounds with pharmacological activity, specific pharmacodynamic and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties to evaluate the drugs and their various applications. Modern artificial intelligence (AI) has the capacity to significantly enhance the role of computational methodology in drug discovery. Use of AI in drug discovery and development, drug repurposing, improving pharmaceutical productivity, and clinical trials will certainly reduce the human workload as well as achieving targets in a short period of time. This chapter elaborates the crosstalk between the machine learning techniques, computational tools and the future of AI in the pharmaceutical industry.

Artificial Intelligence Recognizes β-Lapachone as an Allosteric 5- Lipoxygenase Inhibitor

2018 ◽  
Author(s):  
Gonçalo Bernardes ◽  
Tiago Rodrigues ◽  
Markus Werner ◽  
Jakob Roth ◽  
Eduardo H. G. da Cruz ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Discovery ◽  
Natural Product ◽  
Systems Pharmacology ◽  
Lipoxygenase Inhibitor ◽  
Machine Learning Methods ◽  
Biochemical Assays ◽  
Viable Approach ◽  
Chemical Matter

<div> <div> <div> <p>Chemical matter with often-discarded moieties entails opportunities for drug discovery. Relying on orthogonal ligand-centric machine learning methods, targets were consensually identified as potential counterparts for the fragment-like natural product β-lapachone. Resorting to a comprehensive range of biophysical and biochemical assays, the natural product was validated as a potent, ligand efficient, allosteric and reversible modulator of 5-lipoxygenase (5-LO). Moreover, we provide a rationale for 5-LO-inhibiting chemotypes inspired in the β-lapachone scaffold through a focused analogue library. This work demonstrates the power of artificial intelligence technologies to deconvolute complex phenotypic readouts of clinically relevant chemical matter, leverage natural product-based drug discovery, as an alternative and/or complement to chemoproteomics and as a viable approach for systems pharmacology studies. </p> </div> </div> </div>

scholarly journals Artificial Intelligence Algorithms for Discovering New Active Compounds Targeting TRPA1 Pain Receptors

AI ◽  
2020 ◽  
Vol 1 (2) ◽  
pp. 276-285
Author(s):  
Dragos Paul Mihai ◽  
Cosmin Trif ◽  
Gheorghe Stancov ◽  
Denise Radulescu ◽  
George Mihai Nitulescu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Discovery ◽  
Random Forests ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Oxidized Lipids ◽  
Active Compounds ◽  
Machine Learning Classification

Transient receptor potential ankyrin 1 (TRPA1) is a ligand-gated calcium channel activated by cold temperatures and by a plethora of electrophilic environmental irritants (allicin, acrolein, mustard-oil) and endogenously oxidized lipids (15-deoxy-∆12, 14-prostaglandin J2 and 5, 6-eposyeicosatrienoic acid). These oxidized lipids work as agonists, making TRPA1 a key player in inflammatory and neuropathic pain. TRPA1 antagonists acting as non-central pain blockers are a promising choice for future treatment of pain-related conditions having advantages over current therapeutic choices A large variety of in silico methods have been used in drug design to speed up the development of new active compounds such as molecular docking, quantitative structure-activity relationship models (QSAR), and machine learning classification algorithms. Artificial intelligence methods can significantly improve the drug discovery process and it is an attractive field that can bring together computer scientists and experts in drug development. In our paper, we aimed to develop three machine learning algorithms frequently used in drug discovery research: feedforward neural networks (FFNN), random forests (RF), and support vector machines (SVM), for discovering novel TRPA1 antagonists. All three machine learning methods used the same class of independent variables (multilevel neighborhoods of atoms descriptors) as prediction of activity spectra for substances (PASS) software. The model with the highest accuracy and most optimal performance metrics was the random forest algorithm, showing 99% accuracy and 0.9936 ROC AUC. Thus, our study emphasized that simpler and robust machine learning algorithms such as random forests perform better in correctly classifying TRPA1 antagonists since the dimension of the dependent variables dataset is relatively modest.

scholarly journals Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

2020 ◽  
Vol 25 (6) ◽  
pp. 895-930
Author(s):  
Hyunho Kim ◽  
Eunyoung Kim ◽  
Ingoo Lee ◽  
Bongsung Bae ◽  
Minsu Park ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Discovery ◽  
Data Driven ◽  
Learning Approaches ◽  
Comprehensive Review

scholarly journals Use of Artificial Intelligence and Machine Learning for Discovery of Drugs for Neglected Tropical Diseases

2021 ◽  
Vol 9 ◽  
Author(s):  
David A. Winkler
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Drug Discovery ◽  
Neglected Tropical Diseases ◽  
Tropical Disease ◽  
Neglected Tropical Disease ◽  
Tropical Diseases ◽  
Entry Barrier ◽  
Ongoing Research ◽  
Technological Developments

Neglected tropical diseases continue to create high levels of morbidity and mortality in a sizeable fraction of the world’s population, despite ongoing research into new treatments. Some of the most important technological developments that have accelerated drug discovery for diseases of affluent countries have not flowed down to neglected tropical disease drug discovery. Pharmaceutical development business models, cost of developing new drug treatments and subsequent costs to patients, and accessibility of technologies to scientists in most of the affected countries are some of the reasons for this low uptake and slow development relative to that for common diseases in developed countries. Computational methods are starting to make significant inroads into discovery of drugs for neglected tropical diseases due to the increasing availability of large databases that can be used to train ML models, increasing accuracy of these methods, lower entry barrier for researchers, and widespread availability of public domain machine learning codes. Here, the application of artificial intelligence, largely the subset called machine learning, to modelling and prediction of biological activities and discovery of new drugs for neglected tropical diseases is summarized. The pathways for the development of machine learning methods in the short to medium term and the use of other artificial intelligence methods for drug discovery is discussed. The current roadblocks to, and likely impacts of, synergistic new technological developments on the use of ML methods for neglected tropical disease drug discovery in the future are also discussed.

Artificial Intelligence, Big data and Machine Learning approaches in Preci-sion Medicine & Drug Discovery

2021 ◽  
Vol 22 ◽  
Author(s):  
Anuraj Nayarisseri ◽  
Ravina Khandelwal ◽  
Poonam Tanwar ◽  
Maddala Madhavi ◽  
Diksha Sharma ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Drug Discovery ◽  
Virtual Screening ◽  
Biologically Active ◽  
Classification Model ◽  
Support Vector ◽  
Learning Approaches ◽  
Qsar Modeling

Abstract: Artificial Intelligence revolutionizes the drug development process that can quickly identify potential biologically active compounds from millions of candidate within a short span of time. The present review is an overview based on some applications of Machine Learning based tools such as GOLD, DeepPVP, LIBSVM, etc and the algorithms involved such as support vector machine (SVM), random forest (RF), decision trees and artificial neural networks (ANN) etc in the various stages of drug designing and development. These techniques can be employed in SNP discoveries, drug repurposing, ligand-based drug design (LBDD), Ligand-based Virtual Screening (LBVS) and Structure-based virtual screening (SBVS), Lead identification, quantitative structure-activity relationship (QSAR) modeling, and ADMET analysis. It is demonstrated that SVM exhibited better performance in indicating that the classification model will have great applications on human intesti-nal absorption (HIA) predictions. Successful cases have been reported which demonstrate the efficiency of SVM and RF model in identifying JFD00950 as a novel compound targeting against a colon cancer cell line, DLD-1 by inhibition of FEN1 cytotoxic and cleavage activity. Furthermore, a QSAR model was also used to predicts flavonoid inhibitory effects on AR activity as a potent treatment for diabetes mellitus (DM), using ANN. Hence, in the era of big data, ML approaches evolved as a powerful and efficient way to deal with the huge amounts of generated data from modern drug discovery in order to model small-molecule drugs, Gene Biomarkers, and identifying the novel drug targets for various diseases.

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