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A review of molecular representation in the age of machine learning
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/9783527618279.ch38openalex-snapshot
A Hierarchy of Structure Representations
inferredcites →https://doi.org/10.1002/chem.201605499openalex-snapshot
Neural‐Symbolic Machine Learning for Retrosynthesis and Reaction Prediction
inferredcites →https://doi.org/10.1002/cmdc.200800178openalex-snapshot
On the Art of Compiling and Using 'Drug‐Like' Chemical Fragment Spaces
inferredcites →https://doi.org/10.1002/wcms.36openalex-snapshot
Representation of chemical structures
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Augmentation Is What You Need!
inferredcites →https://doi.org/10.1016/j.drudis.2019.02.013openalex-snapshot
The next level in chemical space navigation: going far beyond enumerable compound libraries
inferredcites →https://doi.org/10.1016/j.drudis.2020.11.037openalex-snapshot
Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 2: a discussion of chemical and biological data
inferredcites →https://doi.org/10.1016/j.drudis.2020.12.009openalex-snapshot
Artificial intelligence in drug discovery: what is realistic, what are illusions? Part 1: Ways to make an impact, and why we are not there yet
inferredcites →https://doi.org/10.1016/j.neucom.2021.04.039openalex-snapshot
Molecular generative Graph Neural Networks for Drug Discovery
inferredcites →https://doi.org/10.1016/j.ymeth.2014.08.005openalex-snapshot
Molecular fingerprint similarity search in virtual screening
inferredcites →https://doi.org/10.1016/s0003-2670(01)83100-7openalex-snapshot
Hose — a novel substructure code
inferredcites →https://doi.org/10.1021/acs.accounts.0c00745openalex-snapshot
Importance of Engineered and Learned Molecular Representations in Predicting Organic Reactivity, Selectivity, and Chemical Properties
inferredcites →https://doi.org/10.1021/acs.chemrev.8b00588openalex-snapshot
Computational Ligand Descriptors for Catalyst Design
inferredcites →https://doi.org/10.1021/acs.jcim.0c00675openalex-snapshot
ZINC20─A Free Ultralarge-Scale Chemical Database for Ligand Discovery
inferredcites →https://doi.org/10.1021/acs.jcim.5b00543openalex-snapshot
Get Your Atoms in Order—An Open-Source Implementation of a Novel and Robust Molecular Canonicalization Algorithm
inferredcites →https://doi.org/10.1021/acs.jcim.6b00601openalex-snapshot
Convolutional Embedding of Attributed Molecular Graphs for Physical Property Prediction
inferredcites →https://doi.org/10.1021/acs.jcim.7b00616openalex-snapshot
Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition
inferredcites →https://doi.org/10.1021/acs.jcim.8b00626openalex-snapshot
De Novo Molecule Design by Translating from Reduced Graphs to SMILES
inferredcites →https://doi.org/10.1021/acs.jcim.9b00286openalex-snapshot
RDChiral: An RDKit Wrapper for Handling Stereochemistry in Retrosynthetic Template Extraction and Application
inferredcites →https://doi.org/10.1021/acs.molpharmaceut.7b01134openalex-snapshot
3D Molecular Representations Based on the Wave Transform for Convolutional Neural Networks
inferredcites →https://doi.org/10.1021/acscentsci.7b00512openalex-snapshot
Generating Focused Molecule Libraries for Drug Discovery with Recurrent Neural Networks
inferredcites →https://doi.org/10.1021/acscentsci.7b00572openalex-snapshot
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules
inferredcites →https://doi.org/10.1021/acscentsci.8b00357openalex-snapshot
Using Machine Learning To Predict Suitable Conditions for Organic Reactions
inferredcites →https://doi.org/10.1021/acscentsci.9b00576openalex-snapshot
Molecular Transformer: A Model for Uncertainty-Calibrated Chemical Reaction Prediction
inferredcites →https://doi.org/10.1021/c160030a007openalex-snapshot
107 Years of Line-Formula Notations (1861-1968)
inferredcites →https://doi.org/10.1021/cen-v030n034.p3523openalex-snapshot
Thw Wiswesser Line Formula Notation
inferredcites →https://doi.org/10.1021/ci00007a012openalex-snapshot
Description of several chemical structure file formats used by computer programs developed at Molecular Design Limited
inferredcites →https://doi.org/10.1021/ci00034a005openalex-snapshot
How the WLN began in 1949 and how it might be in 1999
inferredcites →https://doi.org/10.1021/ci00039a002openalex-snapshot
The CAS ONLINE search system. 1. General system design and selection, generation, and use of search screens
inferredcites →https://doi.org/10.1021/ci00047a024openalex-snapshot
Generic structure storage and retrieval
inferredcites →https://doi.org/10.1021/ci00054a007openalex-snapshot
DARC system: notions of defined and generic substructures. Filiation and coding of FREL substructure (SS) classes
inferredcites →https://doi.org/10.1021/ci00057a005openalex-snapshot
SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules
inferredcites →https://doi.org/10.1021/ci00062a008openalex-snapshot
SMILES. 2. Algorithm for generation of unique SMILES notation
inferredcites →https://doi.org/10.1021/ci010132ropenalex-snapshot
Reoptimization of MDL Keys for Use in Drug Discovery
inferredcites →https://doi.org/10.1021/ci050400bopenalex-snapshot
The Blue Obelisk—Interoperability in Chemical Informatics
inferredcites →https://doi.org/10.1021/ci100384dopenalex-snapshot
Chemical Name to Structure: OPSIN, an Open Source Solution
inferredcites →https://doi.org/10.1021/ci200488kopenalex-snapshot
Accurate Specification of Molecular Structures: The Case for Zero-Order Bonds and Explicit Hydrogen Counting
inferredcites →https://doi.org/10.1021/ci300116popenalex-snapshot
Mining Electronic Laboratory Notebooks: Analysis, Retrosynthesis, and Reaction Based Enumeration
inferredcites →https://doi.org/10.1021/ci3001277openalex-snapshot
ZINC: A Free Tool to Discover Chemistry for Biology
inferredcites →https://doi.org/10.1021/ci3001925openalex-snapshot
HELM: A Hierarchical Notation Language for Complex Biomolecule Structure Representation
inferredcites →https://doi.org/10.1021/ci600238jopenalex-snapshot
Algorithm for Advanced Canonical Coding of Planar Chemical Structures That Considers Stereochemical and Symmetric Information
inferredcites →https://doi.org/10.1021/ci900507gopenalex-snapshot
Comparative Evaluation of 3D Virtual Ligand Screening Methods: Impact of the Molecular Alignment on Enrichment
inferredcites →https://doi.org/10.1021/ci960109jopenalex-snapshot
SYBYL Line Notation (SLN): A Versatile Language for Chemical Structure Representation
inferredcites →https://doi.org/10.1021/ci970429iopenalex-snapshot
RECAPRetrosynthetic Combinatorial Analysis Procedure: A Powerful New Technique for Identifying Privileged Molecular Fragments with Useful Applications in Combinatorial Chemistry
inferredcites →https://doi.org/10.1021/ed100697wopenalex-snapshot
ChemSpider: An Online Chemical Information Resource
inferredcites →https://doi.org/10.1021/ja00051a040openalex-snapshot
UFF, a full periodic table force field for molecular mechanics and molecular dynamics simulations
inferredcites →https://doi.org/10.1021/jm0707727openalex-snapshot
Similarity Searching and Scaffold Hopping in Synthetically Accessible Combinatorial Chemistry Spaces
inferredcites →https://doi.org/10.1038/s41586-021-03213-yopenalex-snapshot
Bayesian reaction optimization as a tool for chemical synthesis
inferredcites →https://doi.org/10.1038/s41586-021-03819-2openalex-snapshot
Highly accurate protein structure prediction with AlphaFold
inferredcites →https://doi.org/10.1038/s41598-017-17299-wopenalex-snapshot
A Universal 3D Voxel Descriptor for Solid-State Material Informatics with Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1038/sdata.2014.22openalex-snapshot
Quantum chemistry structures and properties of 134 kilo molecules
inferredcites →https://doi.org/10.1039/c8sc04175jopenalex-snapshot
Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations
inferredcites →https://doi.org/10.1039/c9me00039aopenalex-snapshot
Deep learning for molecular design—a review of the state of the art
inferredcites →https://doi.org/10.1039/c9sc01844aopenalex-snapshot
Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis
inferredcites →https://doi.org/10.1039/d0cp00305kopenalex-snapshot
Are 2D fingerprints still valuable for drug discovery?
inferredcites →https://doi.org/10.1039/d1sc00231gopenalex-snapshot
Beyond generative models: superfast traversal, optimization, novelty, exploration and discovery (STONED) algorithm for molecules using SELFIES
inferredcites →https://doi.org/10.1088/2632-2153/aba947openalex-snapshot
Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation
inferredcites →https://doi.org/10.1088/2632-2153/ac09d6openalex-snapshot
Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations
inferredcites →https://doi.org/10.1093/bib/bbab365openalex-snapshot
Using molecular embeddings in QSAR modeling: does it make a difference?
inferredcites →https://doi.org/10.1093/nar/gkaa971openalex-snapshot
PubChem in 2021: new data content and improved web interfaces
inferredcites →https://doi.org/10.1093/nar/gkv352openalex-snapshot
ChEMBL web services: streamlining access to drug discovery data and utilities
inferredcites →https://doi.org/10.1093/nar/gky1075openalex-snapshot
ChEMBL: towards direct deposition of bioassay data
inferredcites →https://doi.org/10.1109/5.726791openalex-snapshot
Gradient-based learning applied to document recognition
inferredcites →https://doi.org/10.1126/science.166.3902.178openalex-snapshot
Computer-Assisted Design of Complex Organic Syntheses
inferredcites →https://doi.org/10.1126/science.aar5169openalex-snapshot
Predicting reaction performance in C–N cross-coupling using machine learning
inferredcites →https://doi.org/10.1145/1390156openalex-snapshot
Proceedings of the 25th international conference on Machine learning - ICML '08
inferredcites →https://doi.org/10.1186/1758-2946-4-22openalex-snapshot
Towards a Universal SMILES representation - A standard method to generate canonical SMILES based on the InChI
inferredcites →https://doi.org/10.1186/s13321-015-0057-7openalex-snapshot
Machines first, humans second: on the importance of algorithmic interpretation of open chemistry data
inferredcites →https://doi.org/10.1186/s13321-015-0068-4openalex-snapshot
InChI, the IUPAC International Chemical Identifier
inferredcites →https://doi.org/10.1186/s13321-016-0160-4openalex-snapshot
Jmol SMILES and Jmol SMARTS: specifications and applications
inferredcites →https://doi.org/10.1186/s13321-018-0258-yopenalex-snapshot
Mordred: a molecular descriptor calculator
inferredcites →https://doi.org/10.1186/s13321-021-00512-4openalex-snapshot
STOUT: SMILES to IUPAC names using neural machine translation
inferredcites →https://doi.org/10.1186/s13321-021-00517-zopenalex-snapshot
InChI version 1.06: now more than 99.99% reliable
inferredcites →https://doi.org/10.1371/journal.pcbi.1002380openalex-snapshot
DOGS: Reaction-Driven de novo Design of Bioactive Compounds
inferredcites →https://doi.org/10.26434/chemrxiv.14554803openalex-snapshot
Report on an NIH Workshop on Ultralarge Chemistry Databases
inferredcites →https://doi.org/10.26434/chemrxiv.7097960.v1openalex-snapshot
DeepSMILES: An Adaptation of SMILES for Use in Machine-Learning of Chemical Structures
inferredcites →https://doi.org/10.48550/arxiv.2106.03609openalex-snapshot
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
inferredcites →https://doi.org/10.5445/ir/1000128111openalex-snapshot
Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Using Artificial Intelligence forde novoDrug Design and Retrosynthesis
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Mapping the Binary Covalent Alloy Space to Pursue Superior Nitrogen Reduction Reaction Catalysts
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Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis
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Computer‐Aided Synthesis Planning (CASP) and Machine Learning: Optimizing Chemical Reaction Conditions
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Artificial Intelligence‐Guided Design of Fluorescent Probes for Biomedical Applications
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Integrating Mechanistic Knowledge into Machine Learning Enables Improved Reaction Prediction in Low‐Data Regimes
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Artificial intelligence and machine learning at various stages and scales of process systems engineering
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The European Federation for Medicinal Chemistry and Chemical Biology (EFMC) Best Practice Initiative: Hit to Lead
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Carbon-based molecular properties efficiently predicted by deep learning-based quantum chemical simulation with large language models
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Precision medicine approach for in vitro modeling and computational screening of anti-epileptic drugs in pediatric epilepsy patients with SCN2A (R1629L) mutation
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Machine learning in process systems engineering: Challenges and opportunities
inferred← citeshttps://doi.org/10.1016/j.compchemeng.2025.109007openalex-snapshot
Machine learning-guided space-filling designs for high throughput liquid formulation development
inferred← citeshttps://doi.org/10.1016/j.compchemeng.2025.109266openalex-snapshot
Self-driving laboratories with artificial intelligence: An overview of process systems engineering perspective
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Part II. Systematic development of machine learning models for predicting human and ecotoxicity from in vivo OECD test guideline data
inferred← citeshttps://doi.org/10.1016/j.corsci.2022.110780openalex-snapshot
Cross-Category Prediction of Corrosion Inhibitor Performance Based on Molecular Graph Structures Via a Three-Level Message Passing Neural Network Model
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Multimodal fused deep learning for drug property prediction: Integrating chemical language and molecular graph
inferred← citeshttps://doi.org/10.1016/j.csbj.2024.07.003openalex-snapshot
Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors
inferred← citeshttps://doi.org/10.1016/j.csbj.2025.08.031openalex-snapshot
Leveraging molecular graphs for natural product classification
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Machine learning for drug-target interaction prediction: A comprehensive review of models, challenges, and computational strategies
inferred← citeshttps://doi.org/10.1016/j.drudis.2022.05.005openalex-snapshot
Yes SIR! On the structure–inactivity relationships in drug discovery
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In silico co-crystal design: Assessment of the latest advances
inferred← citeshttps://doi.org/10.1016/j.ecoenv.2025.119008openalex-snapshot
Predicting binding affinities of liquid crystal monomers: An activity cliffs-driven multidimensional feature fusion model
inferred← citeshttps://doi.org/10.1016/j.eij.2024.100558openalex-snapshot
Deep Learning-Assisted Compound Bioactivity Estimation Framework
inferred← citeshttps://doi.org/10.1016/j.ejmech.2025.118176openalex-snapshot
PyaiVS unifies AI workflows to accelerate ligand discovery and yields ABCG2 inhibitors
inferred← citeshttps://doi.org/10.1016/j.envpol.2025.127011openalex-snapshot
Novel graph neural network reveals binding mechanisms and environmental risks of PAHs interaction with estrogen receptor B
inferred← citeshttps://doi.org/10.1016/j.envres.2025.122811openalex-snapshot
Deep learning-Guided optimization of cobalt catalysts for antibiotic degradation
inferred← citeshttps://doi.org/10.1016/j.fluid.2022.113657openalex-snapshot
Predicting PC-SAFT pure-component parameters by machine learning using a molecular fingerprint as key input
inferred← citeshttps://doi.org/10.1016/j.fluid.2026.114754openalex-snapshot
Parameterisation of equations of state for polymers: from experimental approaches to machine learning
inferred← citeshttps://doi.org/10.1016/j.forsciint.2026.112922openalex-snapshot
Explainable machine learning to identify cannabinoid 1 receptor agonists
inferred← citeshttps://doi.org/10.1016/j.heliyon.2024.e39038openalex-snapshot
Transformer-based models for chemical SMILES representation: A comprehensive literature review
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A pre-trained multi-representation fusion network for molecular property prediction
inferred← citeshttps://doi.org/10.1016/j.isci.2025.111881openalex-snapshot
Homogeneous catalyst graph neural network: A human-interpretable graph neural network tool for ligand optimization in asymmetric catalysis
inferred← citeshttps://doi.org/10.1016/j.isci.2026.116937openalex-snapshot
Semantic knowledge improves molecular machine learning for chemical toxicity prediction
inferred← citeshttps://doi.org/10.1016/j.jlp.2025.105876openalex-snapshot
Prediction of the exothermic onset temperature of aromatic nitro compounds by the SiRMS descriptors from molecular structures
inferred← citeshttps://doi.org/10.1016/j.jmgm.2026.109441openalex-snapshot
Integrating molecular representations for machine learning-based virtual screening of Glutaminyl Cyclase inhibitors
inferred← citeshttps://doi.org/10.1016/j.jpha.2025.101313openalex-snapshot
A multimodal contrastive learning framework for predicting P-glycoprotein substrates and inhibitors
inferred← citeshttps://doi.org/10.1016/j.jpowsour.2025.236794openalex-snapshot
A front-end fusion feature-based machine learning engine for rechargeable battery manufacturing accelerates screening of organic electrodes
inferred← citeshttps://doi.org/10.1016/j.knosys.2025.114195openalex-snapshot
LLM-enhanced multi-level knowledge distillation for molecular property prediction
inferred← citeshttps://doi.org/10.1016/j.matt.2025.101958openalex-snapshot
Guiding the rational design of biocompatible metal-organic frameworks for drug delivery
inferred← citeshttps://doi.org/10.1016/j.medidd.2025.100223openalex-snapshot
Revolutionizing pharmacology: AI-powered approaches in molecular modeling and ADMET prediction
inferred← citeshttps://doi.org/10.1016/j.memsci.2024.122927openalex-snapshot
Molecular fingerprint-aided prediction of organic solute rejection in reverse osmosis and nanofiltration
inferred← citeshttps://doi.org/10.1016/j.nanoen.2026.112208openalex-snapshot
Unraveling the design rules for enhancing stability of organic photovoltaic materials under accelerated aging conditions through machine learning
inferred← citeshttps://doi.org/10.1016/j.nantod.2026.102974openalex-snapshot
AI-driven design and applications of quantum dots
inferred← citeshttps://doi.org/10.1016/j.patter.2022.100588openalex-snapshot
SELFIES and the future of molecular string representations
inferred← citeshttps://doi.org/10.1016/j.pmatsci.2025.101544openalex-snapshot
Machine learning in polymer science: A new lens for physical and chemical exploration
inferred← citeshttps://doi.org/10.1016/j.procs.2023.01.023openalex-snapshot
Crop Yield Prediction using Machine Learning and Deep Learning Techniques
inferred← citeshttps://doi.org/10.1016/j.rechem.2023.100905openalex-snapshot
Principal component analysis of quantum mechanical descriptors data to reveal the pharmacological activities of oxindole derivatives
inferred← citeshttps://doi.org/10.1016/j.rechem.2024.101551openalex-snapshot
Machine learning to identify structural motifs in asphaltenes
inferred← citeshttps://doi.org/10.1016/j.rineng.2026.113180openalex-snapshot
Toward autonomous chemical discovery: agentic AI in organic synthesis and nanochemistry
inferred← citeshttps://doi.org/10.1016/j.rpth.2025.102691openalex-snapshot
Structural bioinformatics for rational drug design
inferred← citeshttps://doi.org/10.1016/j.surfin.2026.108417openalex-snapshot
Surface adsorption and corrosion inhibition in copper CMP: A first-principles and data-driven investigation
inferred← citeshttps://doi.org/10.1016/j.swevo.2025.101967openalex-snapshot
RetroEA: An efficient evolutionary algorithm for retrosynthetic route planning
inferred← citeshttps://doi.org/10.1016/j.swevo.2026.102374openalex-snapshot
MBERA: Multi-Branch Evolutionary Retrosynthesis Algorithm
inferred← citeshttps://doi.org/10.1016/j.teler.2026.100294openalex-snapshot
AI without borders: The rise of cross-disciplinary machine learning
inferred← citeshttps://doi.org/10.1016/j.tifs.2024.104578openalex-snapshot
Advances in machine learning screening of food bioactive compounds
inferred← citeshttps://doi.org/10.1016/j.tifs.2025.104887openalex-snapshot
Harnessing AI for enhanced screening of antimicrobial bioactive compounds in food safety and preservation
inferred← citeshttps://doi.org/10.1016/j.tifs.2025.105347openalex-snapshot
Machine learning in the citrus essential oil industry: A comprehensive review on process optimization, authentication, and flavor and bioactive compound development
inferred← citeshttps://doi.org/10.1016/j.trac.2025.118320openalex-snapshot
Artificial intelligence for method development in liquid chromatography
inferred← citeshttps://doi.org/10.1021/acs.accounts.2c00801openalex-snapshot
Molecular Machine Learning for Chemical Catalysis: Prospects and Challenges
inferred← citeshttps://doi.org/10.1021/acs.analchem.5c03958openalex-snapshot
MSformer: A Meta-Structure Based Interpretable Framework for Representation Learning of Natural Products
inferred← citeshttps://doi.org/10.1021/acs.analchem.6c03104openalex-snapshot
ClipMol: A Molecular Representation Learning Framework for CCS Prediction via SMILES–InChI Dual-View Chemical Language Alignment
inferred← citeshttps://doi.org/10.1021/acs.chemmater.4c02754openalex-snapshot
Simulation-Assisted Deep Learning Techniques for Commercially Applicable OLED Phosphorescent Materials
inferred← citeshttps://doi.org/10.1021/acs.chemmater.6c01049openalex-snapshot
Active Learning for Generalizable Detonation Performance Prediction of Energetic Materials
inferred← citeshttps://doi.org/10.1021/acs.chemrestox.6c00093openalex-snapshot
Democratizing Artificial Intelligence in Toxicology: Real-World Applications and Automated Computational Workflows
inferred← citeshttps://doi.org/10.1021/acs.chemrev.3c00189openalex-snapshot
Machine Learning Methods for Small Data Challenges in Molecular Science
inferred← citeshttps://doi.org/10.1021/acs.chemrev.3c00223openalex-snapshot
Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications
inferred← citeshttps://doi.org/10.1021/acs.chemrev.4c00049openalex-snapshot
Toward an AI Era: Advances in Electronic Skins
inferred← citeshttps://doi.org/10.1021/acs.chemrev.4c00893openalex-snapshot
Studying Noncovalent Interactions in Molecular Systems with Machine Learning
inferred← citeshttps://doi.org/10.1021/acs.chemrev.5c00689openalex-snapshot
Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules
inferred← citeshttps://doi.org/10.1021/acs.est.4c14193openalex-snapshot
Machine Learning-Assisted Molecular Structure Embedding for Accurate Prediction of Emerging Contaminant Removal by Ozonation Oxidation
inferred← citeshttps://doi.org/10.1021/acs.est.6c05993openalex-snapshot
Multimodal Machine Learning Models on Aqueous Direct Photodisappearance Rate Constants of Chemicals Exhibit Previously Unattainable Performance
inferred← citeshttps://doi.org/10.1021/acs.iecr.3c01565openalex-snapshot
Outlook: How I Learned to Love Machine Learning (A Personal Perspective on Machine Learning in Process Systems Engineering)
inferred← citeshttps://doi.org/10.1021/acs.iecr.6c01601openalex-snapshot
Molecular Property Prediction via Sparse Binary Matrix Representation and Convolutional Neural Networks
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c00643openalex-snapshot
Open-Source Machine Learning in Computational Chemistry
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c00892openalex-snapshot
Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH 3 /H/O Radicals
inferred← citeshttps://doi.org/10.1021/acs.jcim.3c01496openalex-snapshot
Deep Generative Models in De Novo Drug Molecule Generation
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00422openalex-snapshot
Drug–Target Interactions Prediction at Scale: The Komet Algorithm with the LCIdb Dataset
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c00424openalex-snapshot
molli: A General Purpose Python Toolkit for Combinatorial Small Molecule Library Generation, Manipulation, and Feature Extraction
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c01396openalex-snapshot
Large Language Models as Molecular Design Engines
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c01907openalex-snapshot
Natural Language Processing Methods for the Study of Protein–Ligand Interactions
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c02102openalex-snapshot
Molecular Dynamics (MD)-Derived Features for Canonical and Noncanonical Amino Acids
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c02255openalex-snapshot
General Chemically Intuitive Atom- and Bond-Level DFT Descriptors for Machine Learning Approaches to Reaction Condition Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.4c02261openalex-snapshot
Improving the Reliability of Molecular String Representations for Generative Chemistry
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00167openalex-snapshot
Prediction of Chromatographic Retention Time of a Small Molecule from SMILES Representation Using a Hybrid Transformer-LSTM Model
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00354openalex-snapshot
Representation of Molecules by Sequences of Instructions
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00484openalex-snapshot
Enhancing Drug-Target Interaction Prediction through Transfer Learning from Activity Cliff Prediction Tasks
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00555openalex-snapshot
Hypershape Recognition: A General Framework for Moment-Based Molecular Similarity
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00584openalex-snapshot
Molecular Optimization Based on a Monte Carlo Tree Search and Multiobjective Genetic Algorithm
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c00645openalex-snapshot
3D Spatial Learning for Adsorption Energy Prediction in Multi-Temporal Solution Systems: The MTSS Data Set and a GCN-Based Network
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c01404openalex-snapshot
Improving Reaction Yield Prediction with Chemical Atom-Level Reaction Learning
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c01613openalex-snapshot
CSMILES: A Compact, Human-Readable SMILES Extension for Conformations
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c02307openalex-snapshot
Chemical Space of Molecular Nanomotors: Optimizing Photochemical Properties for One- and Two-Photon Applications
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c02342openalex-snapshot
gSelformer-MV: Multiview, Subgraph-Augmented Group SELFIES Transformer for Molecular Property Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c02523openalex-snapshot
StereoMolGraph: Stereochemistry-Aware Molecular and Reaction Graphs
inferred← citeshttps://doi.org/10.1021/acs.jcim.5c02959openalex-snapshot
Integrating Multiview Information for Enhanced Deep Learning-Based Acute Dermal Toxicity Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c00227openalex-snapshot
Ensemble Machine Learning for Interpretable Prediction of Acute Toxicity in Metal–Organic Framework Linkers
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c01026openalex-snapshot
NMR-AI: An Open Platform for NMR-Enhanced Molecular Representations and Physicochemical Property Prediction
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c01094openalex-snapshot
Machine learning for RNA-targeting drug design
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c01209openalex-snapshot
Self-Supervised Learning for Molecular Property Prediction: Methods, Multimodal Insights, and Benchmark Comparisons
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c01878openalex-snapshot
Unimodal vs Multimodal Learning: A Systematic Evaluation of Fusion Strategies and Model Design for Molecular Property Prediction and Uncertainty Quantification
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c02092openalex-snapshot
An Inverse Design Workflow for Solvent Mixtures Based on Hansen Solubility Parameters
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c02178openalex-snapshot
ElemeNet: Multiscale Molecular Machine Learning with Uncertainty Quantification across the Periodic Table
inferred← citeshttps://doi.org/10.1021/acs.jcim.6c02361openalex-snapshot
In-Context Learning Meets Small Molecule Property Prediction: Benchmarking Novel Machine Learning Approaches
inferred← citeshttps://doi.org/10.1021/acs.jctc.5c00178openalex-snapshot
Learning Multiple Potential Energy Surfaces by Automated Discovery of a Compatible Representation
inferred← citeshttps://doi.org/10.1021/acs.jctc.5c00303openalex-snapshot
Data-Driven Virtual Screening of Conformational Ensembles of Transition-Metal Complexes
inferred← citeshttps://doi.org/10.1021/acs.jctc.5c00425openalex-snapshot
Neural Mulliken Analysis: Molecular Graphs from Density Matrices for QSPR on Raw Quantum-Chemical Data
inferred← citeshttps://doi.org/10.1021/acs.jctc.5c01652openalex-snapshot
Machine Learning Assisted Selective Configuration Interaction for Accurate Ground and Excited State Calculations
inferred← citeshttps://doi.org/10.1021/acs.joc.5c03206openalex-snapshot
Best Practices and Considerations for Applying Multiple Linear Regression in Organic Chemistry Research
inferred← citeshttps://doi.org/10.1021/acs.jpca.2c08250openalex-snapshot
Quantitative In Silico Prediction of the Rate of Protodeboronation by a Mechanistic Density Functional Theory-Aided Algorithm
inferred← citeshttps://doi.org/10.1021/acs.jpca.3c04779openalex-snapshot
Recent Applications of Machine Learning in Molecular Property and Chemical Reaction Outcome Predictions
inferred← citeshttps://doi.org/10.1021/acs.jpca.4c00912openalex-snapshot
Predicting Rate Constants of Alkane Cracking Reactions Using Machine Learning
inferred← citeshttps://doi.org/10.1021/acs.jpca.4c03580openalex-snapshot
Automated Mixture Analysis via Structural Evaluation
inferred← citeshttps://doi.org/10.1021/acs.jpca.4c05718openalex-snapshot
Transferability Across Different Molecular Systems and Levels of Theory with the Data-Driven Coupled-Cluster Scheme
inferred← citeshttps://doi.org/10.1021/acs.jpca.6c03025openalex-snapshot
Unraveling the Electronic Origin of Selectivity in Ambimodal Transition States with Valence Bond Theory
inferred← citeshttps://doi.org/10.1021/acs.jpcb.6c01292openalex-snapshot
Fluoride Electrolyte Discovery via Reactivity Guided Genetic Algorithms
inferred← citeshttps://doi.org/10.1021/acs.jpclett.5c01077openalex-snapshot
Interpretable Multimodal Deep Ensemble Framework Dissecting Bloodbrain Barrier Permeability with Molecular Features
inferred← citeshttps://doi.org/10.1021/acs.jpclett.5c03797openalex-snapshot
Molecular Representation Matters: Comparative Evaluation of Fingerprints, RDKit Descriptors, and Hashing Effects
inferred← citeshttps://doi.org/10.1021/acs.jpclett.6c02427openalex-snapshot
A Multitask Large Reasoning Model for Molecular Science
inferred← citeshttps://doi.org/10.1021/acscatal.4c00864openalex-snapshot
Decoding the Kinetic Complexity of Pt-Catalyzed n-Butane Dehydrogenation by Machine Learning and Microkinetic Analysis
inferred← citeshttps://doi.org/10.1021/acscentsci.3c00050openalex-snapshot
Accelerated Chemical Reaction Optimization Using Multi-Task Learning
inferred← citeshttps://doi.org/10.1021/acscentsci.5c00561openalex-snapshot
Reasoning Language Model as Rule Finder: A Case Study on C–H Bond Activation Using 2D Metal–Organic Frameworks
inferred← citeshttps://doi.org/10.1021/acscentsci.5c00785openalex-snapshot
SERS Cheminformatics: Opportunities for Data-Driven Discovery and Applications
inferred← citeshttps://doi.org/10.1021/acschemneuro.5c00649openalex-snapshot
Machine Learning–Based Bioactivity Prediction and Descriptor-Guided Rational Design of Amyloid-β Aggregation Inhibitors
inferred← citeshttps://doi.org/10.1021/acsestwater.5c00850openalex-snapshot
PHYSPROPNET: A Benchmarking Study of Machine Learning Models for Physicochemical Property Prediction in Data-Limited Environmental Research
inferred← citeshttps://doi.org/10.1021/acsinfocus.7e7006openalex-snapshot
Molecular Representations for Machine Learning
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