ReviewPharmaceutical research2016
The Next Era: Deep Learning in Pharmaceutical Research.
Review in Pharmaceutical research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 77 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
77 citing papers in PubMed.
- Toward an AI Era: Application of Artificial Intelligence in Inclusion Complex Screening.Pharmaceutics · 2026Review
- Modelling and estimation of chemical reaction yields from high-throughput experiments.Communications chemistry · 2026Article
- Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications.Frontiers in bioinformatics · 2026Review
- AΙ-Driven Drug Repurposing: Applications and Challenges.Medicines (Basel, Switzerland) · 2025Review
- Experimental and computational approaches for evaluating molecule interactions with equilibrative nucleoside transporters 1 and 2.The Journal of pharmacology and experimental therapeutics · 2025Article
- The Pharmaceutical Industry's Future: How Artificial Intelligence is Transforming Medicine.Advanced pharmaceutical bulletin · 2025Review
- Computational Approaches for Predicting Drug Interactions with Human Organic Anion Transporter 4 (OAT4).Molecular pharmaceutics · 2025Article
- Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.Biomarker research · 2025Review
- Prediction and Prioritisation of Novel Anthelmintic Candidates from Public Databases Using Deep Learning and Available Bioactivity Data Sets.International journal of molecular sciences · 2025Article
- Addressing docking pose selection with structure-based deep learning: Recent advances, challenges and opportunities.Computational and structural biotechnology journal · 2024Review
- The Goldilocks paradigm: comparing classical machine learning, large language models, and few-shot learning for drug discovery applications.Communications chemistry · 2024Article
- A prediction model based on artificial intelligence techniques for disintegration time and hardness of fast disintegrating tablets in pre-formulation tests.BMC medical informatics and decision making · 2024Article
- Absorption Distribution Metabolism Excretion and Toxicity Property Prediction Utilizing a Pre-Trained Natural Language Processing Model and Its Applications in Early-Stage Drug Development.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Recent advancements toward the incremsent of drug solubility using environmentally-friendly supercritical COFrontiers in medicine · 2024Review
- Recent Advances in Pharmaceutical Design: Unleashing the Potential of Novel Therapeutics.Current pharmaceutical biotechnology · 2024Review
- Force Field Analysis Software and Tools (FFAST): Assessing Machine Learning Force Fields under the Microscope.Journal of chemical theory and computation · 2023Article
- Face the Future-Artificial Intelligence in Oral and Maxillofacial Surgery.Journal of clinical medicine · 2023Review
- Electron density mapping of boron clustersRSC advances · 2023Article
- Dataset development of pre-formulation tests on fast disintegrating tablets (FDT): data aggregation.BMC research notes · 2023Article
- SSELM-neg: spherical search-based extreme learning machine for drug-target interaction prediction.BMC bioinformatics · 2023Article
17 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule's properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.