OOIR: Observatory of International Research

Papers

(The H4-Index of Journal of Cheminformatics is 36. The table below lists those papers that are above that threshold based on CrossRef citation counts [max. 250 papers]. The publications cover those that have been published in the past four years, i.e., from 2022-05-01 to 2026-05-01.)

Article	Citations
Evolve with your research: stepwise system evolution from document-driven to fact-centric research data management in materials science	216
Assessing interaction recovery of predicted protein-ligand poses	117
Predicting chemical structure using reinforcement learning with a stack-augmented conditional variational autoencoder	113
Explainable uncertainty quantifications for deep learning-based molecular property prediction	90
Equivariant diffusion for structure-based de novo ligand generation with latent-conditioning	89
Moldina: a fast and accurate search algorithm for simultaneous docking of multiple ligands	89
ANNalog: generation of MedChem-similar molecules	84
Generating diversity and securing completeness in algorithmic retrosynthesis	74
HERGAI: an artificial intelligence tool for structure-based prediction of hERG inhibitors	68
European Registry of Materials: global, unique identifiers for (undisclosed) nanomaterials	68
Biosynfoni: a biosynthesis-informed and interpretable lightweight molecular fingerprint	63
Exploring the ability of machine learning-based virtual screening models to identify the functional groups responsible for binding	60
Dimensionally reduced machine learning model for predicting single component octanol–water partition coefficients	60
An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification	60
Determining the parent and associated fragment formulae in mass spectrometry via the parent subformula graph	56
Privileged structure-based molecular fingerprints for organic electronic materials: towards intuitive machine learning interpretation	55
Paths to cheminformatics: Q&A with Ann M. Richard	51
Deep learning of multimodal networks with topological regularization for drug repositioning	49
APBIO: bioactive profiling of air pollutants through inferred bioactivity signatures and prediction of novel target interactions	48
MolPrice: assessing synthetic accessibility of molecules based on market value	47
PMF-CPI: assessing drug selectivity with a pretrained multi-functional model for compound–protein interactions	47
AdapTor: Adaptive Topological Regression for quantitative structure–activity relationship modeling	47
MLinvitroTox reloaded for high-throughput hazard-based prioritization of high-resolution mass spectrometry data	46
AutoTemplate: enhancing chemical reaction datasets for machine learning applications in organic chemistry	45
Novel molecule design with POWGAN, a policy-optimized Wasserstein generative adversarial network	44

VSFlow: an open-source ligand-based virtual screening tool	42
Machine learning to predict food effects during drug development: a comprehensive review	41
Computer-aided pattern scoring (C@PS): a novel cheminformatic workflow to predict ligands with rare modes-of-action	41
AiZynthFinder 4.0: developments based on learnings from 3 years of industrial application	41
NPBS Atlas: a comprehensive data resource for exploring the biological sources of natural products	40
Fifteen years of ChEMBL and its role in cheminformatics and drug discovery	40
One chiral fingerprint to find them all	39
Analysis of the benefits of imputation models over traditional QSAR models for toxicity prediction	39
A quantum chemical dataset of interacting molecular pairs for chemical reaction studies	38
Crossover operators for molecular graphs with an application to virtual drug screening	37
Implementation of an open chemistry knowledge base with a Semantic Wiki	37
Shinyscreen: mass spectrometry data inspection and quality checking utility	36
NanoBinder: a machine learning assisted nanobody binding prediction tool using Rosetta energy scores	36
Explaining compound activity predictions with a substructure-aware loss for graph neural networks	36