Machine Learning in Drug Discovery: A Review

Dara, Suresh; Dhamercherla, Swetha; Jadav, Surender Singh; Babu, Ch. Madhu; Ahsan, Mohamed Jawed

doi:10.1007/s10462-021-10058-4

Cited by 252 publications

(157 citation statements)

References 243 publications

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“…These technologies can be further strengthened by ML approaches to refine target identification and validation and enhance the likelihood of clinical success. [ 119 ]…”

Section: Target Discovery and Validationmentioning

confidence: 99%

Hepatic fibrosis 2022: Unmet needs and a blueprint for the future

2022

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Steady progress over four decades toward understanding the pathogenesis and clinical consequences of hepatic fibrosis has led to the expectation of effective antifibrotic drugs, yet none has been approved. Thus, an assessment of the field is timely, to clarify priorities and accelerate progress. Here, we highlight the successes to date but, more importantly, identify gaps and unmet needs, both experimentally and clinically. These include the need to better define cell-cell interactions and etiology-specific elements of fibrogenesis and their link to disease-specific drivers of portal hypertension. Success in treating viral hepatitis has revealed the remarkable capacity of the liver to degrade scar in reversing fibrosis, yet we know little of the mechanisms underlying this response. Thus, there is an exigent need to clarify the cellular and molecular mechanisms of fibrosis regression in order for therapeutics to mimic the liver's endogenous capacity. Better refined and more predictive in vitro and animal models will hasten drug development. From a clinical perspective, current diagnostics are improving but not always biologically plausible or sufficiently accurate to supplant biopsy. More urgently, digital pathology methods that leverage machine learning and artificial intelligence must be validated in order to capture more prognostic information from liver biopsies and better quantify the response to therapies. For more refined treatment of NASH, orthogonal approaches that integrate genetic, clinical, and pathological data sets may yield treatments for specific subphenotypes of the disease.Collectively, these and other advances will strengthen and streamline clinical trials and better link histologic responses to clinical outcomes.

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“…These technologies can be further strengthened by ML approaches to refine target identification and validation and enhance the likelihood of clinical success. [ 119 ]…”

Section: Target Discovery and Validationmentioning

confidence: 99%

Hepatic fibrosis 2022: Unmet needs and a blueprint for the future

2022

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“…Dara et. al., [184] discuss the opportunities provided by machine learning in the discovery of new drugs. These papers discuss one specific application area within the healthcare domain.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence: A Review of Progress and Prospects in Medicine and Healthcare

Mishra¹

2022

j.electron.electromedical.eng.med.inform

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Andrew NG, a leading philosopher in the field of Artificial Intelligence (AI) once quoted “AI is the new electricity” which has the potential to transform and drive every industry. The most important driving factor for the AI transformation will be data. Clive Humby, a data science entrepreneur was once quoted saying “data is the new oil” and data analytics being the “combustion engine” will drive the AI led innovations. The rapid rise of Artificial Intelligence technologies in the past decade, has inspired industries to invest in every opportunity for integrating AI solutions to their products. Research, development, and innovation in the field of AI are shaping various industries like automobile, manufacturing, finance, retail, supply chain management, and education among others. The healthcare industry has also been adopting the ways of AI into various workflows within the domain. With the evolution in computing and processing powers coupled with hardware modernizations, the adoption of AI looks more feasible than ever. Research and Innovations are happening in almost every field of healthcare and hospital workflows with the target of making healthcare processes more efficient & accessible, increase the overall state of healthcare, reduce physician stress levels, and increase the patient satisfaction levels. The conventional ways in which healthcare and clinical workflows have been operating are now starting to see the change with the integration of many data driven AI solutions. The digital innovations are making life easy for healthcare professionals allowing them to spend more time listening to the problems of patients and consequently increasing the patient satisfaction levels. However, there are limitations and concerns on security of Protected Health Information which have to be addressed for a seamless amalgamation of AI systems into the healthcare domain. Many papers have been published which mostly talk about one particular field/problem in the healthcare domain. No publications have covered the opportunities provided by AI technologies to the entire healthcare domain. This review paper discusses in detail about the progress AI has been able to make in the healthcare domain holistically and what the future of AI looks like. The paper also discusses about the implementation opportunities various AI technologies like Machine Learning, Deep Learning, Reinforcement Learning, Natural Language Processing, Computer Vision provide in different fields of healthcare and clinical workflows and how Artificial Intelligence systems will boost the capabilities of healthcare professionals in restoring the human touch in patient-physician encounters. A physician’s intuition and judgement will always remain better suited since each case, each health condition, and each person is unique in its own way, but AI methods can help enhance the accuracy of diagnosis, assist physicians in making improved and precise clinical decisions.

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“…This is a time taking process which may affect the further procedure. ML approaches are not so convenient for high‐level, symbolic reasoning, or planning which creates difficulty to understand all aspects of natural language (Dara et al, 2021).…”

Section: Recent Advances and Limitationsmentioning

confidence: 99%

Machine learning approaches and their applications in drug discovery and design

et al. 2022

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This review is focused on several machine learning approaches used in chemoinformatics. Machine learning approaches provide tools and algorithms to improve drug discovery. Many physicochemical properties of drugs like toxicity, absorption, drug‐drug interaction, carcinogenesis, and distribution have been effectively modeled by QSAR techniques. Machine learning is a subset of artificial intelligence, and this technique has shown tremendous potential in the field of drug discovery. Techniques discussed in this review are capable of modeling non‐linear datasets, as well as big data of increasing depth and complexity. Various machine learning‐based approaches are being used for drug target prediction, modeling the structure of drug target, binding site prediction, ligand‐based similarity searching, de novo designing of ligands with desired properties, developing scoring functions for molecular docking, building QSAR model for biological activity prediction, and prediction of pharmacokinetic and pharmacodynamic properties of ligands. In recent years, these predictive tools and models have achieved good accuracy. By the use of more related input data, relevant parameters, and appropriate algorithms, the accuracy of these predictions can be further improved.

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Machine Learning in Drug Discovery: A Review

Cited by 252 publications

References 243 publications

Hepatic fibrosis 2022: Unmet needs and a blueprint for the future

Hepatic fibrosis 2022: Unmet needs and a blueprint for the future

Artificial Intelligence: A Review of Progress and Prospects in Medicine and Healthcare

Machine learning approaches and their applications in drug discovery and design

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