ArticleJournal of chemical sciences (Bangalore, India)2022
Artificial intelligence: machine learning for chemical sciences.
Article in Journal of chemical sciences (Bangalore, India), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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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.
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Who cites it
17 citing papers in PubMed, 90 citations in OpenAlex.
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- Perspective on applicability of data-driven machine learning computational new approach methodologies for hazard identification in chemicals risk assessment.Journal of cheminformatics · 2026Review
- Artificial Intelligence in Analytical Chemistry: Towards Yet Undiscovered Opportunities.Analytical chemistry · 2026Review
- Exploration of Novel Chemical Spaces to Discover JAK1 Inhibitors: An Ensemble Docking-Guided Deep Learning Approach.ACS omega · 2026Article
- Machine learning approach to topological graph descriptors of graphene nanoribbons.Frontiers in chemistry · 2026Article
- Integrating chemical artificial intelligence and cognitive computing for predictive analysis of biological pathways: a case for intrinsically disordered proteins.Biophysical reviews · 2025Review
- Experimental Design (2Polymers · 2025Article
- A review of large language models and autonomous agents in chemistry.Chemical science · 2025Review
- Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case.Angewandte Chemie (International ed. in English) · 2024Article
- Article
- Identification of Phospholipids Relevant to Cancer Tissue Using Differential Ion Mobility Spectrometry.International journal of molecular sciences · 2024Article
- PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications.Scientific data · 2024Article
- Machine Learning Methods for Small Data Challenges in Molecular Science.Chemical reviews · 2023Review
- Molecular Understanding and Practical In Silico Catalyst Design in Computational Organocatalysis and Phase Transfer Catalysis-Challenges and Opportunities.Molecules (Basel, Switzerland) · 2023Review
- Generative model based on junction tree variational autoencoder for HOMO value prediction and molecular optimization.Journal of cheminformatics · 2023Article
- Improving Chemical Reaction Prediction with Unlabeled Data.Molecules (Basel, Switzerland) · 2022Article
- Materials Discovery With Machine Learning and Knowledge Discovery.Frontiers in chemistry · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Research in molecular sciences witnessed the rise and fall of Artificial Intelligence (AI)/ Machine Learning (ML) methods, especially artificial neural networks, few decades ago. However, we see a major resurgence in the use of modern ML methods in scientific research during the last few years. These methods have had phenomenal success in the areas of computer vision, speech recognition, natural language processing (NLP), etc. This has inspired chemists and biologists to apply these algorithms to problems in natural sciences. Availability of high performance Graphics Processing Unit (GPU) accelerators, large datasets, new algorithms, and libraries has enabled this surge. ML algorithms have successfully been applied to various domains in molecular sciences by providing much faster and sometimes more accurate solutions compared to traditional methods like Quantum Mechanical (QM) calculations, Density Functional Theory (DFT) or Molecular Mechanics (MM) based methods, etc. Some of the areas where the potential of ML methods are shown to be effective are in drug design, prediction of high-level quantum mechanical energies, molecular design, molecular dynamics materials, and retrosynthesis of organic compounds, etc. This article intends to conceptually introduce various modern ML methods and their relevance and applications in computational natural sciences.
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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.