Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening DOI
Shaherin Basith, Balachandran Manavalan, Tae Hwan Shin

et al.

Medicinal Research Reviews, Journal Year: 2020, Volume and Issue: 40(4), P. 1276 - 1314

Published: Jan. 10, 2020

Discovery and development of biopeptides are time-consuming, laborious, dependent on various factors. Data-driven computational methods, especially machine learning (ML) approach, can rapidly efficiently predict the utility therapeutic peptides. ML methods offer an array tools that accelerate enhance decision making discovery for well-defined queries with ample sophisticated data quality. Various approaches, such as support vector machines, random forest, extremely randomized tree, more recently deep useful in peptide-based drug discovery. These approaches leverage peptide sets, created via high-throughput sequencing enable prediction functional peptides increased levels accuracy. The use therapeutics is relatively recent; however, these techniques already revolutionizing protein research by unraveling their novel functions. In this review, we discuss several ML-based state-of-the-art peptide-prediction compare terms algorithms, feature encodings, scores, evaluation methodologies, software utilities. We also assessed performance using well-constructed independent sets. addition, common pitfalls challenges therapeutics. Overall, show models streamline targeted therapies.

Language: Английский

Artificial intelligence in digital pathology — new tools for diagnosis and precision oncology DOI
Kaustav Bera, Kurt A. Schalper, David L. Rimm

et al.

Nature Reviews Clinical Oncology, Journal Year: 2019, Volume and Issue: 16(11), P. 703 - 715

Published: Aug. 9, 2019

Language: Английский

Citations

1142
Artificial intelligence to deep learning: machine intelligence approach for drug discovery DOI Creative Commons

Rohan Gupta,

Devesh Srivastava, Mehar Sahu

et al.

Molecular Diversity, Journal Year: 2021, Volume and Issue: 25(3), P. 1315 - 1360

Published: April 12, 2021

Drug designing and development is an important area of research for pharmaceutical companies chemical scientists. However, low efficacy, off-target delivery, time consumption, high cost impose a hurdle challenges that impact drug design discovery. Further, complex big data from genomics, proteomics, microarray data, clinical trials also obstacle in the discovery pipeline. Artificial intelligence machine learning technology play crucial role development. In other words, artificial neural networks deep algorithms have modernized area. Machine been implemented several processes such as peptide synthesis, structure-based virtual screening, ligand-based toxicity prediction, monitoring release, pharmacophore modeling, quantitative structure-activity relationship, repositioning, polypharmacology, physiochemical activity. Evidence past strengthens implementation this field. Moreover, novel mining, curation, management techniques provided critical support to recently developed modeling algorithms. summary, advancements provide excellent opportunity rational process, which will eventually mankind. The primary concern associated with consumption production cost. inefficiency, inaccurate target inappropriate dosage are hurdles inhibit process delivery With technology, computer-aided integrating can eliminate traditional referred superset comprising learning, whereas comprises supervised unsupervised reinforcement learning. subset has extensively network, vector machines, classification regression, generative adversarial networks, symbolic meta-learning examples applied process. different areas synthesis molecule design, screening molecular docking, relationship protein misfolding protein-protein interactions, pathway identification polypharmacology. principles active inactive, pre-clinical development, secondary biomarker manufacturing, bioactivity properties, prediction toxicity, mode action.

Language: Английский

Citations

919
Why 90% of clinical drug development fails and how to improve it? DOI Creative Commons
Duxin Sun, Wei Gao, Hongxiang Hu

et al.

Acta Pharmaceutica Sinica B, Journal Year: 2022, Volume and Issue: 12(7), P. 3049 - 3062

Published: Feb. 11, 2022

Ninety percent of clinical drug development fails despite implementation many successful strategies, which raised the question whether certain aspects in target validation and optimization are overlooked? Current overly emphasizes potency/specificity using structure‒activity-relationship (SAR) but overlooks tissue exposure/selectivity disease/normal tissues structure‒tissue exposure/selectivity–relationship (STR), may mislead candidate selection impact balance dose/efficacy/toxicity. We propose exposure/selectivity–activity relationship (STAR) to improve optimization, classifies candidates based on drug's potency/selectivity, exposure/selectivity, required dose for balancing efficacy/toxicity. Class I drugs have high specificity/potency needs low achieve superior efficacy/safety with success rate. II requires efficacy toxicity be cautiously evaluated. III relatively (adequate) manageable often overlooked. IV achieves inadequate efficacy/safety, should terminated early. STAR studies development.

Language: Английский

Citations

859
Concepts of Artificial Intelligence for Computer-Assisted Drug Discovery DOI Creative Commons
Xin Yang, Yifei Wang, Ryan Byrne

et al.

Chemical Reviews, Journal Year: 2019, Volume and Issue: 119(18), P. 10520 - 10594

Published: July 11, 2019

Artificial intelligence (AI), and, in particular, deep learning as a subcategory of AI, provides opportunities for the discovery and development innovative drugs. Various machine approaches have recently (re)emerged, some which may be considered instances domain-specific AI been successfully employed drug design. This review comprehensive portrayal these techniques their applications medicinal chemistry. After introducing basic principles, alongside application notes, various algorithms, current state-of-the art AI-assisted pharmaceutical is discussed, including structure- ligand-based virtual screening, de novo design, physicochemical pharmacokinetic property prediction, repurposing, related aspects. Finally, several challenges limitations methods are summarized, with view to potential future directions

Language: Английский

Citations

766
Machine Learning: New Ideas and Tools in Environmental Science and Engineering DOI
Shifa Zhong, Kai Zhang, Majid Bagheri

et al.

Environmental Science & Technology, Journal Year: 2021, Volume and Issue: unknown

Published: Aug. 17, 2021

The rapid increase in both the quantity and complexity of data that are being generated daily field environmental science engineering (ESE) demands accompanied advancement analytics. Advanced analysis approaches, such as machine learning (ML), have become indispensable tools for revealing hidden patterns or deducing correlations which conventional analytical methods face limitations challenges. However, ML concepts practices not been widely utilized by researchers ESE. This feature explores potential to revolutionize modeling ESE field, covers essential knowledge needed applications. First, we use five examples illustrate how addresses complex problems. We then summarize four major types applications ESE: making predictions; extracting importance; detecting anomalies; discovering new materials chemicals. Next, introduce required current shortcomings ESE, with a focus on three important but often overlooked components when applying ML: correct model development, proper interpretation, sound applicability analysis. Finally, discuss challenges future opportunities application highlight this field.

Language: Английский

Citations

672
Multimodal biomedical AI DOI Open Access
Julián Acosta, Guido J. Falcone, Pranav Rajpurkar

et al.

Nature Medicine, Journal Year: 2022, Volume and Issue: 28(9), P. 1773 - 1784

Published: Sept. 1, 2022

Language: Английский

Citations

572
Computational approaches streamlining drug discovery DOI Creative Commons
Anastasiia Sadybekov, Vsevolod Katritch

Nature, Journal Year: 2023, Volume and Issue: 616(7958), P. 673 - 685

Published: April 26, 2023

Computer-aided drug discovery has been around for decades, although the past few years have seen a tectonic shift towards embracing computational technologies in both academia and pharma. This is largely defined by flood of data on ligand properties binding to therapeutic targets their 3D structures, abundant computing capacities advent on-demand virtual libraries drug-like small molecules billions. Taking full advantage these resources requires fast methods effective screening. includes structure-based screening gigascale chemical spaces, further facilitated iterative approaches. Highly synergistic are developments deep learning predictions target activities lieu receptor structure. Here we review recent advances technologies, potential reshaping whole process development, as well challenges they encounter. We also discuss how rapid identification highly diverse, potent, target-selective ligands protein can democratize process, presenting new opportunities cost-effective development safer more small-molecule treatments. Recent approaches application streamlining discussed.

Language: Английский

Citations

564
Machine Learning in Drug Discovery: A Review DOI Open Access
Suresh Dara,

Swetha Dhamercherla,

Surender Singh Jadav

et al.

Artificial Intelligence Review, Journal Year: 2021, Volume and Issue: 55(3), P. 1947 - 1999

Published: Aug. 11, 2021

Language: Английский

Citations

478
Molecular contrastive learning of representations via graph neural networks DOI
Yuyang Wang, Jianren Wang, Zhonglin Cao

et al.

Nature Machine Intelligence, Journal Year: 2022, Volume and Issue: 4(3), P. 279 - 287

Published: March 3, 2022

Language: Английский

Citations

450
Artificial intelligence and machine learning in clinical development: a translational perspective DOI Creative Commons
Pratik Shah,

Francis Kendall,

Sean Khozin

et al.

npj Digital Medicine, Journal Year: 2019, Volume and Issue: 2(1)

Published: July 26, 2019

Abstract Future of clinical development is on the verge a major transformation due to convergence large new digital data sources, computing power identify clinically meaningful patterns in using efficient artificial intelligence and machine-learning algorithms, regulators embracing this change through collaborations. This perspective summarizes insights, recent developments, recommendations for infusing actionable computational evidence into health care from academy, biotechnology industry, nonprofit foundations, regulators, technology corporations. Analysis learning publically available biomedical trial sets, real-world sensors, records by architectures are discussed. Strategies modernizing process integration AI- ML-based methods secure technologies recently announced regulatory pathways at United States Food Drug Administration outlined. We conclude discussing applications impact algorithmic improve medical patients.

Language: Английский

Citations

407