Active learning enables generation of molecules that advance the known Pareto front

No abstract is available for this paper.

• Publication year

  2026

• Venue

  npj Computational Materials

• Publication date

  2026-02-10

• Fields of study

  Not labeled

• Identifiers
  DOI 10.1038/s41524-025-01924-8
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Realistic material property prediction using domain adaptation based machine learning

  2024cited by this paper
• Sample efficient reinforcement learning with active learning for molecular design

  2024cited by this paper
• Inverse Design of Singlet‐Fission Materials with Uncertainty‐Controlled Genetic Optimization

  2024cited by this paper
• Optimal Molecular Design: Generative Active Learning Combining REINVENT with Precise Binding Free Energy Ranking Simulations

  2024cited by this paper
• Directly optimizing for synthesizability in generative molecular design using retrosynthesis models

  2024cited by this paper
• Generative Active Learning for the Search of Small-molecule Protein Binders

  2024cited by this paper
• Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study

  2024cited by this paper
• Inverse design of copolymers including stoichiometry and chain architecture

  2024cited by this paper
• Virtual Screening Algorithms in Drug Discovery: A Review Focused on Machine and Deep Learning Methods

  2023cited by this paper
• Chemprop: A Machine Learning Package for Chemical Property Prediction

  2023cited by this paper
• Generative organic electronic molecular design informed by quantum chemistry

  2023cited by this paper
• Geometric Latent Diffusion Models for 3D Molecule Generation

  2023cited by this paper
• Generative models for molecular discovery: Recent advances and challenges

  2022cited by this paper
• Regression Transformer enables concurrent sequence regression and generation for molecular language modelling

  2022cited by this paper
• By how much can closed-loop frameworks accelerate computational materials discovery?

  2022cited by this paper
• Tartarus: A Benchmarking Platform for Realistic And Practical Inverse Molecular Design

  2022influential reference
• Deep learning for development of organic optoelectronic devices: efficient prescreening of hosts and emitters in deep-blue fluorescent OLEDs

  2022cited by this paper
• Machine-learning-guided early drug discovery of small molecules.

  2022cited by this paper
• Artificial intelligence–enabled virtual screening of ultra-large chemical libraries with deep docking

  2022cited by this paper
• Parallel tempered genetic algorithm guided by deep neural networks for inverse molecular design

  2021cited by this paper
• Integrating synthetic accessibility with AI-based generative drug design

  2021cited by this paper
• Predicting Energetics Materials' Crystalline Density from Chemical Structure by Machine Learning

  2021cited by this paper
• Data Augmentation and Pretraining for Template-Based Retrosynthetic Prediction in Computer-Aided Synthesis Planning

  2020cited by this paper
• The Synthesizability of Molecules Proposed by Generative Models

  2020cited by this paper
• AiiDA 1.0, a scalable computational infrastructure for automated reproducible workflows and data provenance

  2020cited by this paper
• Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization

  2020cited by this paper
• Retrosynthetic accessibility score (RAscore) – rapid machine learned synthesizability classification from AI driven retrosynthetic planning†

  2020cited by this paper
• Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES

  2020cited by this paper
• REINVENT 2.0: An AI Tool for De Novo Drug Design

  2020cited by this paper
• Analyzing Learned Molecular Representations for Property Prediction

  2019cited by this paper
• Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation

  2019cited by this paper
• Constrained Bayesian optimization for automatic chemical design using variational autoencoders

  2019cited by this paper
• SCScore: Synthetic Complexity Learned from a Reaction Corpus

  2018cited by this paper
• Inverse molecular design using machine learning: Generative models for matter engineering

  2018cited by this paper
• Large-scale comparison of machine learning methods for drug target prediction on ChEMBL

  2018cited by this paper
• Computational Chemical Synthesis Analysis and Pathway Design

  2018cited by this paper
• Junction Tree Variational Autoencoder for Molecular Graph Generation

  2018cited by this paper
• Neural Message Passing for Quantum Chemistry

  2017cited by this paper
• MoleculeNet: a benchmark for molecular machine learning

  2017cited by this paper
• Neural-Symbolic Machine Learning for Retrosynthesis and Reaction Prediction.

  2017cited by this paper
• Molecular de-novo design through deep reinforcement learning

  2017influential reference
• The Cambridge Structural Database

  2016cited by this paper
• Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

  2016cited by this paper
• AiiDA: Automated Interactive Infrastructure and Database for Computational Science

  2015influential reference
• Lead candidates for high-performance organic photovoltaics from high-throughput quantum chemistry – the Harvard Clean Energy Project

  2014cited by this paper
• Quantum chemistry structures and properties of 134 kilo molecules

  2014cited by this paper
• NWChem

  2011cited by this paper
• Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds

  2011cited by this paper
• NWChem: A comprehensive and scalable open-source solution for large scale molecular simulations

  2010cited by this paper
• Estimation of synthetic accessibility score of drug-like molecules based on molecular complexity and fragment contributions

  2009cited by this paper
• Method for measurement of the density of thin films of small organic molecules.

  2007cited by this paper
• Improved prediction of heats of formation of energetic materials using quantum mechanical calculations.

  2006cited by this paper
• Publisher's Note

  2003cited by this paper
• Molecular surface electrostatic potentials in relation to noncovalent interactions in biological systems

  2001cited by this paper
• The art and practice of structure‐based drug design: A molecular modeling perspective

  1996cited by this paper
• Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization

  year unknowncited by this paper

• No citing papers are available for this paper.