ReviewJournal of the American Chemical Society2023
Generative Models as an Emerging Paradigm in the Chemical Sciences.
Review in Journal of the American Chemical Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 87 papers.
What it found
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Who cites it
87 citing papers in PubMed.
- Big Data and Artificial Intelligence in Cancer Drug Discovery: Promise, Challenges, and Emerging Opportunities.Cancers · 2026Review
- Computational data as the fuel for AI in chemistry.Chemical science · 2026Review
- Artificial intelligence and ultra-high performance computing methods and experiments for drug discovery: virtual screening, deep learning, molecular dynamics simulations, ADMET modelling, and experimental validation.Molecular biomedicine · 2026Review
- Multi-objective optimization in the context of generative chemistry.Nature communications · 2026Review
- Review
- Replacing Quantum Chemistry With Machine-Learned Interatomic Potentials: Revolution or Evolution?ACS central science · 2026Review
- Guided Adaptive Diffusion: An Evolutionary Framework for Multimodal Atomistic Structure Prediction.Journal of chemical information and modeling · 2026Article
- Modular Framework for 3D Molecular Generation in Computational Chemistry Applications.Journal of the American Chemical Society · 2026Article
- Evaluating AI-Generated Molecules for Drug Discovery: From Generic Metrics to Translational Readiness.International journal of molecular sciences · 2026Review
- From mechanistic modeling to AI-driven design: computational strategies for targeting the γ-secretase complex.Briefings in bioinformatics · 2026Review
- NaviDiv: a web app for monitoring chemical diversity in generative molecular design.Digital discovery · 2026Article
- Quantum Chemistry-Driven Molecular Inverse Design of Stable Isomers with Data-Free Reinforcement Learning.Journal of chemical theory and computation · 2026Article
- Generative AI-Driven Discovery of Next-Generation Electrolytes for Alkali Metal Batteries.Journal of chemical information and modeling · 2026Article
- Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models.Angewandte Chemie (International ed. in English) · 2026Review
- Machine Learning for Designing Perovskites and Perovskite-Inspired Solar Materials: Emerging Opportunities and Challenges.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Boosting Computational Catalysis and Chemical Reactivity with Artificial Intelligence.Journal of the American Chemical Society · 2026Review
- A review of recent advances in generative artificial intelligence models for biomolecular sciences.Acta pharmaceutica Sinica. B · 2026Review
- Inverse Molecular Design for the Discovery of Organic Energy Transfer Photocatalysts: Bridging Global and Local Chemical Space Exploration.Journal of the American Chemical Society · 2026Article
- Article
27 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional computational approaches to design chemical species are limited by the need to compute properties for a vast number of candidates, e.g., by discriminative modeling. Therefore, inverse design methods aim to start from the desired property and optimize a corresponding chemical structure. From a machine learning viewpoint, the inverse design problem can be addressed through so-called generative modeling. Mathematically, discriminative models are defined by learning the probability distribution function of properties given the molecular or material structure. In contrast, a generative model seeks to exploit the joint probability of a chemical species with target characteristics. The overarching idea of generative modeling is to implement a system that produces novel compounds that are expected to have a desired set of chemical features, effectively sidestepping issues found in the forward design process. In this contribution, we overview and critically analyze popular generative algorithms like generative adversarial networks, variational autoencoders, flow, and diffusion models. We highlight key differences between each of the models, provide insights into recent success stories, and discuss outstanding challenges for realizing generative modeling discovered solutions in chemical applications.
Identifiers
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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.