CoChat
Sign Up
Cited by 429Computational Drug Discovery MethodsMachine Learning in Materials ScienceArtificial Intelligence in Healthcare and Education

The DOI matches its Crossref record; 471 of the 473 references it cites were found in a registry or an index.

Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions

Open access
Vatansever, Sezen, Schlessinger, Avner, Wacker, Daniel, Kaniskan, H. Ümit, Jin, Jian, Zhou, Ming‐Ming, Zhang, Bin·2021·Medicinal Research Reviews
DOI 10.1002/med.21764PMC PMC8043990
Open this paper in CoChat — free accountOpen PDF

Abstract

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML-driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML-powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state-of-the-art of AI/ML-guided CNS drug discovery, focusing on blood-brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1002/med.21764

Cites

471 of 471 references verified0 citation errors · 0 not checkable

Statistics

Figures

Figure 1 AI/ML applications in the drug discovery pipeline. AI/ML approaches provide a range of tools that can be applied in all the three stages of early drug discovery to improve decision making and speed up the process. ADME, absorption, distribution, metabolism, and excretion; AI, artificial intelligence; ML, machine learning; QSAR, quantitative structure–activity relationship
Figure 1 AI/ML applications in the drug discovery pipeline. AI/ML approaches provide a range of tools that can be applied in all the three stages of early drug discovery to improve decision making and speed up the process. ADME, absorption, distribution, metabolism, and excretion; AI, artificial intelligence; ML, machine learning; QSAR, quantitative structure–activity relationshipCC BY · PubMed Central
Figure 2 The basic steps of building an artificial intelligence (AI) platform for drug discovery. The process for developing an AI model as follows: (1) Define the problem appropriately (objective, desired outputs, etc.), (2) prepare the data (collection, exploration and profiling, formatting, and improving the quality), (3) transform raw data into features and select meaningful features (a.k.a. feature engineering), (4) split data into training and validation sets, (5) develop a model, (6) train the model with a fraction of the data, test its performance (cross‐validation) and tune its parameters with the validation set (7) evaluate model performance on the validation set and refine the model, and (8) evaluate the model on independent data not used for method development
Figure 2 The basic steps of building an artificial intelligence (AI) platform for drug discovery. The process for developing an AI model as follows: (1) Define the problem appropriately (objective, desired outputs, etc.), (2) prepare the data (collection, exploration and profiling, formatting, and improving the quality), (3) transform raw data into features and select meaningful features (a.k.a. feature engineering), (4) split data into training and validation sets, (5) develop a model, (6) train the model with a fraction of the data, test its performance (cross‐validation) and tune its parameters with the validation set (7) evaluate model performance on the validation set and refine the model, and (8) evaluate the model on independent data not used for method developmentCC BY · PubMed Central
Figure 3 AI‐guided target discovery. AI/ML methods can efficiently analyze all available information to speed up the discovery of disease‐related drug targets. Specifically, AI/ML methods are utilized for disease subtyping, identification of disease driver genes and microRNAs, alternative splicing prediction, triaging of novel drug targets, modeling of three‐dimensional target structures, and druggability assessment. AI, artificial intelligence; ML, machine learning
Figure 3 AI‐guided target discovery. AI/ML methods can efficiently analyze all available information to speed up the discovery of disease‐related drug targets. Specifically, AI/ML methods are utilized for disease subtyping, identification of disease driver genes and microRNAs, alternative splicing prediction, triaging of novel drug targets, modeling of three‐dimensional target structures, and druggability assessment. AI, artificial intelligence; ML, machine learningCC BY · PubMed Central
Figure 4 AI/ML‐enabled improvements in the treatment of CNS diseases. DL is a subset of ML, which is a subset of AI and their applications address a wide range of challenges in CNS drug discovery and development. The application fields portrayed here are discussed in the Section 3. AI, artificial intelligence; CNS, central nervous system; DL, deep learning; ML, machine learning
Figure 4 AI/ML‐enabled improvements in the treatment of CNS diseases. DL is a subset of ML, which is a subset of AI and their applications address a wide range of challenges in CNS drug discovery and development. The application fields portrayed here are discussed in the Section 3. AI, artificial intelligence; CNS, central nervous system; DL, deep learning; ML, machine learningCC BY · PubMed Central

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 473 references

Research works printed but not linked: 4

  1. Todeschini R, 2001, Books—handbook of molecular descriptors, Angew Chem, 40, 1977
  2. Matsumoto S, 2020, Extraction of protein dynamics information hidden in Cryo‐EM map using deep learning, bioRxiv
  3. DahlGE JaitlyN SalakhutdinovRMulti‐task neural networks for QSAR predictions.arXiv preprint arXiv:14061231.2014.
  4. 10.1038/s41563-019-0332-5 · DOI

Cited by

Sorted by citations

Related

See all

Metadata

Authors
Vatansever, S., Schlessinger, A., Wacker, D., Kaniskan, H. Ü., Jin, J., Zhou, M., & Zhang, B.
Affiliations
  • Icahn School of Medicine at Mount Sinai (US)
  • Department of Pharmacological Sciences Icahn School of Medicine at Mount Sinai New York New York USA
  • Tisch Cancer Institute (US)
Corresponding author
Zhang, Bin
ORCID
Venue
Medicinal Research Reviews
Publisher
Wiley
Published
2020-12-09
Volume
41
Issue
3
Pages
1427-1473
ISSN
  • 0198-6325 (ISSN-L)
  • 1098-1128
DOI
10.1002/med.21764
PMC
PMC8043990
OpenAlex
W3112951420
Cited by
429
References
473
Citations by year
2026: 64 · 2025: 126 · 2024: 127 · 2023: 62 · 2022: 34 · 2021: 16
FWCI
2.48
Type
journal article
Language
en
Open access
Licence
Locations
  • Medicinal Research Reviews (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health
  • National Institute on Aging (U01AG058635, R01AG057907, U01AG046170, RF1AG054014, R01AG068030, RF1AG057440, RF1AG059319, R01AG062355, U01AG052411, R01AG072562)
  • National Institute of General Medical Sciences (R35GM133504)
  • National Institute of Allergy and Infectious Diseases (R01AI124465)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
artificial intelligence in drug discovery, machine learning, central nervous system disorders, neurological disorders, target identification, compound screening, drug synergy prediction, de novo drug design, drug repurposing, drug attrition
MeSH terms
Machine Learning, Algorithms, Artificial Intelligence, Central Nervous System Diseases, Humans, Drug Discovery

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review