default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDJournal of Cheminformatics, Volume 17
Volume 17, Number 1, December 2025
- Vincenzo Vigna, Tânia F. G. G. Cova, A. A. C. C. Pais, Emilia Sicilia:
Prediction of Pt, Ir, Ru, and Rh complexes light absorption in the therapeutic window for phototherapy using machine learning. 1 - Zishuo Zeng, Jin Guo, Jiao Jin, Xiaozhou Luo:
CLAIRE: a contrastive learning-based predictor for EC number of chemical reactions. 2 - Dong Wang, Jieyu Jin, Guqin Shi, Jingxiao Bao, Zheng Wang, Shimeng Li, Peichen Pan, Dan Li, Yu Kang, Tingjun Hou:
ADMET evaluation in drug discovery: 21. Application and industrial validation of machine learning algorithms for Caco-2 permeability prediction. 3 - Matteo P. Ferla, Ruben Sanchez-Garcia
, Rachael Skyner, Stefan Gahbauer, Jenny C. Taylor, Frank von Delft, Brian D. Marsden, Charlotte M. Deane:
Fragmenstein: predicting protein-ligand structures of compounds derived from known crystallographic fragment hits using a strict conserved-binding-based methodology. 4 - Atsushi Yoshimori, Jürgen Bajorath:
Context-dependent similarity analysis of analogue series for structure-activity relationship transfer based on a concept from natural language processing. 5 - Jean-Louis Reymond:
Chemical space as a unifying theme for chemistry. 6 - James Wellnitz, Sankalp Jain, Joshua E. Hochuli, Travis Maxfield, Eugene N. Muratov, Alexander Tropsha, Alexey V. Zakharov:
One size does not fit all: revising traditional paradigms for assessing accuracy of QSAR models used for virtual screening. 7 - Jochem Nelen, Horacio Pérez Sánchez, Hans De Winter, Dries Van Rompaey:
Matched pairs demonstrate robustness against inter-assay variability. 8 - Pablo Rodríguez-Belenguer, Emilio Soria-Olivas, Manuel Pastor:
StreamChol: a web-based application for predicting cholestasis. 9 - Farjana Tasnim Mukta, Md. Masud Rana, Avery Meyer, Sally Ellingson, Duc Duy Nguyen:
The algebraic extended atom-type graph-based model for precise ligand-receptor binding affinity prediction. 10 - Dohyeon Lee, Sunyong Yoo:
hERGAT: predicting hERG blockers using graph attention mechanism through atom- and molecule-level interaction analyses. 11 - Rahul Brahma, Sunghyun Moon, Jae-Min Shin, Kwang-Hwi Cho:
AiGPro: a multi-tasks model for profiling of GPCRs for agonist and antagonist. 12 - Eva Viesi, Ugo Perricone, Patrick Aloy, Rosalba Giugno:
APBIO: bioactive profiling of air pollutants through inferred bioactivity signatures and prediction of novel target interactions. 13 - Katarzyna Arturi, Eliza J. Harris, Lilian Gasser, Beate I. Escher, Georg Braun, Robin Bosshard, Juliane Hollender:
MLinvitroTox reloaded for high-throughput hazard-based prioritization of high-resolution mass spectrometry data. 14 - Javier Corvi, Nicolás Díaz-Roussel, José M. Fernández, Francesco Ronzano, Emilio Centeno, Pablo Accuosto, Celine Ibrahim, Shoji Asakura, Frank Bringezu, Mirjam Fröhlicher, Annika Kreuchwig, Yoko Nogami, Jeong Rih, Raul Rodriguez-Esteban, Nicolas Sajot, Jörg Wichard, Heng-Yi Michael Wu, Philip Drew, Thomas Steger-Hartmann, Alfonso Valencia, Laura I. Furlong, Salvador Capella-Gutiérrez:
PretoxTM: a text mining system for extracting treatment-related findings from preclinical toxicology reports. 15 - Milan Picard, Mickaël Leclercq, Antoine Bodein, Marie-Pier Scott-Boyer, Olivier Périn, Arnaud Droit:
Improving drug repositioning with negative data labeling using large language models. 16 - Medard Edmund Mswahili, Junha Hwang, Jagath C. Rajapakse, Kyuri Jo, Young-Seob Jeong:
Positional embeddings and zero-shot learning using BERT for molecular-property prediction. 17 - Maximilian G. Schuh, Davide Boldini, Annkathrin I. Bohne, Stephan A. Sieber:
Barlow Twins deep neural network for advanced 1D drug-target interaction prediction. 18 - Rafal Mulka, Dan Su, Wen-Shuo Huang, Li Zhang, Huaihai Huang, Xiaoyu Lai, Yao Li, Xiao-Song Xue:
FluoBase: a fluorinated agents database. 19 - Fang-Yuan Sun, Ying-Hao Yin, Hui-Jun Liu, Lu-Na Shen, Xiu-Lin Kang, Gui-Zhong Xin, Li-Fang Liu, Jia-Yi Zheng:
ROASMI: accelerating small molecule identification by repurposing retention data. 20 - Liam Brydon, Kunyang Zhang, Gillian Dobbie, Katerina Taskova, Jörg Simon Wicker:
Predictive modeling of biodegradation pathways using transformer architectures. 21 - Romeo Cozac, Haris Hasic, Jun Jin Choong, Vincent Richard, Loic Beheshti, Cyrille Froehlich, Takuto Koyama, Shigeyuki Matsumoto, Ryosuke Kojima, Hiroaki Iwata, Aki Hasegawa, Takao Otsuka, Yasushi Okuno:
kMoL: an open-source machine and federated learning library for drug discovery. 22 - Marie Oestreich, Erinc Merdivan, Michael Lee, Joachim L. Schultze, Marie Piraud, Matthias Becker:
DrugDiff: small molecule diffusion model with flexible guidance towards molecular properties. 23 - Dev Punjabi, Yu-Chieh Huang, Laura Holzhauer, Pierre Tremouilhac, Pascal Friederich, Nicole Jung, Stefan Bräse:
Infrared spectrum analysis of organic molecules with neural networks using standard reference data sets in combination with real-world data. 24 - Alessio Fallani, Ramil I. Nugmanov, Jose A. Arjona-Medina, Jörg Kurt Wegner, Alexandre Tkatchenko, Kostiantyn Chernichenko:
Pretraining graph transformers with atom-in-a-molecule quantum properties for improved ADMET modeling. 25 - Paula Torren-Peraire, Jonas Verhoeven, Dorota Herman, Hugo Ceulemans, Igor V. Tetko, Jörg K. Wegner:
Improving route development using convergent retrosynthesis planning. 26 - Xiaodan Yin, Xiaorui Wang, Zhenxing Wu, Qin Li, Yu Kang, Yafeng Deng, Pei Luo, Huanxiang Liu, Guqin Shi, Zheng Wang, Xiaojun Yao, Chang-Yu Hsieh, Tingjun Hou:
Syn-MolOpt: a synthesis planning-driven molecular optimization method using data-derived functional reaction templates. 27 - Andrew T. McNutt, Yanjing Li, Rocco Meli, Rishal Aggarwal, David Ryan Koes:
GNINA 1.3: the next increment in molecular docking with deep learning. 28 - Hannah Rosa Friesacher, Ola Engkvist, Lewis H. Mervin, Yves Moreau, Adam Arany:
Achieving well-informed decision-making in drug discovery: a comprehensive calibration study using neural network-based structure-activity models. 29 - Gregory W. Kyro, Matthew T. Martin, Eric D. Watt, Victor S. Batista:
CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability. 30 - Mikhail Andronov, Natalia Andronova, Michael Wand, Jürgen Schmidhuber, Djork-Arné Clevert:
Accelerating the inference of string generation-based chemical reaction models for industrial applications. 31 - Barbara Zdrazil:
Fifteen years of ChEMBL and its role in cheminformatics and drug discovery. 32 - Francesco Codicè, Corrado Pancotti, Cesare Rollo, Yves Moreau, Piero Fariselli, Daniele Raimondi:
The specification game: rethinking the evaluation of drug response prediction for precision oncology. 33 - Rayyan T. Khan, Pavel Kohout, Milos Musil, Monika Rosinska, Jirí Damborský, Stanislav Mazurenko, David Bednar:
Anticipating protein evolution with successor sequence predictor. 34 - Caiya Zhang, Yan Sun, Pingzhao Hu:
An interpretable deep geometric learning model to predict the effects of mutations on protein-protein interactions using large-scale protein language model. 35 - Shoichi Ishida, Tomohiro Sato, Teruki Honma, Kei Terayama:
Large language models open new way of AI-assisted molecule design for chemists. 36 - Muniba Batool, Naveed Ahmed Azam, Jianshen Zhu, Kazuya Haraguchi, Liang Zhao, Tatsuya Akutsu:
A unified approach to inferring chemical compounds with the desired aqueous solubility. 37 - Fabian Krüger, Johan Östman, Lewis H. Mervin, Igor V. Tetko, Ola Engkvist:
Publishing neural networks in drug discovery might compromise training data privacy. 38 - Huynh Anh Duy, Tarapong Srisongkram:
Protecting your skin: a highly accurate LSTM network integrating conjoint features for predicting chemical-induced skin irritation. 39 - Antony J. Williams, Ann M. Richard:
Three pillars for ensuring public access and integrity of chemical databases powering cheminformatics. 40 - Alan Kai Hassen, Martin Sícho, Yorick J. van Aalst, Mirjam C. W. Huizenga, Darcy N. R. Reynolds, Sohvi Luukkonen, Andrius Bernatavicius, Djork-Arné Clevert, Antonius P. A. Janssen, Gerard J. P. van Westen, Mike Preuss:
Generate what you can make: achieving in-house synthesizability with readily available resources in de novo drug design. 41 - Said Byadi, P. K. Hashim, Pavel Sidorov:
Predictive modeling of visible-light azo-photoswitches' properties using structural features. 42 - Eva Viesi, Ugo Perricone, Patrick Aloy, Rosalba Giugno:
Correction: APBIO: bioactive profiling of air pollutants through inferred bioactivity signatures and prediction of novel target interactions. 43 - Christoph Steinbeck:
The evolution of open science in cheminformatics: a journey from closed systems to collaborative innovation. 44 - Gufeng Yu, Kaiwen Yu, Xi Wang, Chenxi Zhang, Yicong Luo, Xiaohong Huo, Yang Yang:
Clc-db: an open-source online database of chiral ligands and catalysts. 45 - Rachelle J. Bienstock:
AI/ML methodologies and the future-will they be successful in designing the next generation of new chemical entities? 46 - Stijn Dhondt, José Oramas, Hans De Winter:
A beginner's approach to deep learning applied to VS and MD techniques. 47 - Jiyeon Han, Wonho Zhung, Insoo Jang, Joongwon Lee, Min Ji Kang, Timothy Dain Lee, Seung Jun Kwack, Kyu-Bong Kim, Daehee Hwang, Byungwook Lee, Hyung Sik Kim, Woo Youn Kim, Sanghyuk Lee:
HepatoToxicity Portal (HTP): an integrated database of drug-induced hepatotoxicity knowledgebase and graph neural network-based prediction model. 48 - Seungchan An, Yeonjin Lee, Junpyo Gong, Seok Young Hwang, In Guk Park, Jayhyun Cho, Min Ju Lee, Minkyu Kim, Yun Pyo Kang, Minsoo Noh:
InertDB as a generative AI-expanded resource of biologically inactive small molecules from PubChem. 49 - Samo Lesnik, Marko Jukic, Urban Bren:
Unveiling polyphenol-protein interactions: a comprehensive computational analysis. 50 - Youngchun Kwon, Hyunjeong Jeon, Joonhyuk Choi, Youn-Suk Choi, Seokho Kang:
Enhancing chemical reaction search through contrastive representation learning and human-in-the-loop. 51 - Xiuyuan Hu, Guoqing Liu, Yang Zhao, Hao Zhang:
Activity cliff-aware reinforcement learning for de novo drug design. - Maria Zavadskaya, Anastasia Orlova, Andrei Dmitrenko, Vladimir Vinogradov:
Integrating QSAR modelling with reinforcement learning for Syk inhibitor discovery. - Jérôme Rihon, Sten Reynders, Vitor Bernardes Pinheiro, Eveline Lescrinier:
The pucke.rs toolkit to facilitate sampling the conformational space of biomolecular monomers. - Jianguo Hu, Yiqing Zhang, Jinxin Xie, Zhen Yuan, Zhangxiang Yin, Shanshan Shi, Honglin Li, Shiliang Li:
Learning motif features and topological structure of molecules for metabolic pathway prediction. - Nadin Ulrich, Karsten Voigt, Anton Kudria, Alexander Böhme, Ralf-Uwe Ebert:
Prediction of the water solubility by a graph convolutional-based neural network on a highly curated dataset. - Hiroaki Shiokawa, Shoichi Ishida, Kei Terayama:
GESim: ultrafast graph-based molecular similarity calculation via von Neumann graph entropy. 57 - Muhammad Arslan Masood, Samuel Kaski, Tianyu Cui:
Molecular property prediction using pretrained-BERT and Bayesian active learning: a data-efficient approach to drug design. 58 - Gergana Tancheva, Vesa Hongisto, Konrad Patyra, Luchesar Iliev, Nikolay T. Kochev, Penny Nymark, Pekka Kohonen, Nina Jeliazkova, Roland Grafstrom:
High-throughput screening data generation, scoring and FAIRification: a case study on nanomaterials. 59 - Jessica Stacey, Baptiste Canault, Stephen D. Pickett, Valerie J. Gillet:
Visualising lead optimisation series using reduced graphs. 60 - Radek Halfar, Jirí Damborský, Sérgio M. Marques, Jan Martinovic:
Moldina: a fast and accurate search algorithm for simultaneous docking of multiple ligands. 61 - Sarah Szwarc, Adriano Rutz, Kyungha Lee, Yassine Mejri, Olivier Bonnet, Hazrina Hazni, Adrien Jagora, Rany B. Mbeng Obame, Jin Kyoung Noh, Elvis Otogo N'Nang, Stephenie C. Alaribe, Khalijah Awang, Guillaume Bernadat, Young Hae Choi, Vincent Courdavault, Michel Frederich, Thomas Gaslonde, Florian Huber, Toh-Seok Kam, Yun Yee Low, Erwan Poupon, Justin J. J. van der Hooft, Kyo Bin Kang, Pierre Le Pogam, Mehdi A. Beniddir:
Translating community-wide spectral library into actionable chemical knowledge: a proof of concept with monoterpene indole alkaloids. 62 - Maria H. Rasmussen, Magnus Strandgaard, Julius Seumer, Laura K. Hemmingsen, Angelo Frei, David Balcells, Jan H. Jensen:
SMILES all around: structure to SMILES conversion for transition metal complexes. 63 - Vincenzo Palmacci, Yasmine Nahal, Matthias Welsch, Ola Engkvist, Samuel Kaski, Johannes Kirchmair:
E-GuARD: expert-guided augmentation for the robust detection of compounds interfering with biological assays. 64 - Konstantin Ushenin, Kuzma Khrabrov, Artem Tsypin, Anton Ber, Egor Rumiantsev, Artur Kadurin:
LAGNet: better electron density prediction for LCAO-based data and drug-like substances. 65 - Elpri Eka Permadi, Reiko Watanabe, Kenji Mizuguchi:
Improving the accuracy of prediction models for small datasets of Cytochrome P450 inhibition with deep learning. 66 - Kirill E. Medvedev, R. Dustin Schaeffer, Nick V. Grishin:
Leveraging AI to explore structural contexts of post-translational modifications in drug binding. 67 - Nikoletta-Maria Koutroumpa, Andreas Tsoumanis, Haralambos Sarimveis, Iseult Lynch, Georgia Melagraki, Antreas Afantitis:
Prediction of blood-brain barrier and Caco-2 permeability through the Enalos Cloud Platform: combining contrastive learning and atom-attention message passing neural networks. 68 - Mehrsa Mardikoraem, Joelle N. Eaves, Theodore Belecciu, Nathaniel Pascual, Alexander Aljets, Bruno Hagenbuch, Erik M. Shapiro, Benjamin J. Orlando, Daniel R. Woldring:
Predicting inhibitors of OATP1B1 via heterogeneous OATP-ligand interaction graph neural network (HOLIgraph). 69 - Gina Ryu, Wankyu Kim:
Application of 3D atom pair map in an attention model for enhanced drug virtual screening. 70 - Murat Kocak, Zafer Akçali:
The published role of artificial intelligence in drug discovery and development: a bibliometric and social network analysis from 1990 to 2023. 71 - Florian Mrugalla, Christopher Franz, Yannic Alber, Georg Mogk, Martín Villalba, Thomas Mrziglod, Kevin Schewior:
Generating diversity and securing completeness in algorithmic retrosynthesis. 72 - Jackson W. Burns, William H. Green Jr.:
Generalizable, fast, and accurate DeepQSPR with fastprop. 73 - Sumin Ha, Dongmin Bang, Sun Kim:
Fate-tox: fragment attention transformer for E(3)-equivariant multi-organ toxicity prediction. 74 - Chia-Lin Lin, Pei-Chi Huang, Christof Wöll, Patrick Théato, Christian Kübel, Lena Pilz, Nicole Jung, Stefan Bräse:
Addressing standardization and semantics in an electronic lab notebook for multidisciplinary use: LabIMotion. 75 - David Errington, Constantin Schneider, Cédric Bouysset, Frédéric A. Dreyer:
Assessing interaction recovery of predicted protein-ligand poses. 76 - Pei-Hua Wang, Wei-Yeh Wu, Che-Yu Lee, Jia-Cheng Hong, Yufeng Jane Tseng:
Advantages of two quantum programming platforms in quantum computing and quantum chemistry. 77 - Mengyang Qu, Gyanendra Sharma, Naoki Wada, Hisaki Ikebata, Shigeyuki Matsunami, Kenji Takahashi:
Machine learning-driven generation and screening of potential ionic liquids for cellulose dissolution. 78 - Qingliang Li, Sunghwan Kim, Leonid Zaslavsky, Tiejun Cheng, Bo Yu, Evan E. Bolton:
A resource description framework (RDF) model of named entity co-occurrences in biomedical literature and its integration with PubChemRDF. 79 - Cecile Valsecchi, Jose A. Arjona-Medina, Natalia Dyubankova, Ramil I. Nugmanov:
Benchmarking molecular conformer augmentation with context-enriched training: graph-based transformer versus GNN models. 80 - Areen Rasool, Jamshaid Ul Rahman, Rongin Uwitije:
Enhancing molecular property prediction with quantized GNN models. 81 - Pilleriin Peets, Aristeidis Litos, Kai Dührkop, Daniel R. Garza, Justin J. J. van der Hooft, Sebastian Böcker, Bas E. Dutilh:
Chemical characteristics vectors map the chemical space of natural biomes from untargeted mass spectrometry data. 82 - Atsushi Yoshimori, Jürgen Bajorath:
Context-dependent similarity searching for small molecular fragments. 83 - Achmad Anggawirya Alimin, Kattariya Srasamran, Wanutchaya Yuenyong, Ampira Charoensaeng, Bor-Jier Shiau, Uthaiporn Suriyapraphadilok:
Surfactant representation using COSMO screened charge density for adsorption isotherm prediction using Physics-Informed Neural Network (PINN). 84 - Alejandro Martínez León, Benjamin Ries, Jochen S. Hub, Aniket Magarkar:
Moldrug algorithm for an automated ligand binding site exploration by 3D aware molecular enumerations. 85 - Martin Starman, Fabian Kirchner, Martin Held, Catriona Eschke, Sayed-Ahmad Sahim, Regine Willumeit-Römer, Nicole Jung, Stefan Bräse:
ELNdataBridge: facilitating data exchange and collaboration by linking Electronic Lab Notebooks via API. 86 - Maryam Astero, Juho Rousu:
Enhancing atom mapping with multitask learning and symmetry-aware deep graph matching. 87 - Alicia Olivares-Gil, José Antonio Barbero-Aparicio, Juan José Rodríguez, José-Francisco Díez-Pastor, César García-Osorio, Mehdi D. Davari:
Semi-supervised prediction of protein fitness for data-driven protein engineering. 88 - Alexandre Varnek, Gilles Marcou, Dragos Horvath:
Higher education in chemoinformatics: achievements and challenges. 89 - Tuan Le, Julian Cremer, Djork-Arné Clevert, Kristof T. Schütt:
Equivariant diffusion for structure-based de novo ligand generation with latent-conditioning. 90 - Eduardo Illueca Fernández, Antonio Jesús Jara Valera, Jesualdo Tomás Fernández-Breis:
Representation of chemistry transport models simulations using knowledge graphs. 91 - Lun Zhu, Qingchao Zhang, Sen Yang:
RLSuccSite: succinylation sites prediction based on reinforcement learning dynamic with balanced reward mechanism and three-peaks enhanced method for physicochemical property scores. 92 - Yongna Yuan, Xiaohang Pan, Xiaohong Li, Ruisheng Zhang, Wei Su:
A 3D generation framework using diffusion model and reinforcement learning to generate multi-target compounds with desired properties. 93 - Qianrong Guo, Saiveth Hernández-Hernández, Pedro J. Ballester:
UMAP-based clustering split for rigorous evaluation of AI models for virtual screening on cancer cell lines. 94 - Raheel Hammad, Sownyak Mondal:
Advancements in thermochemical predictions: a multi-output thermodynamics-informed neural network approach. 95 - Palistha Shrestha, Chandana S. Talwar, Jeevan Kandel, Kwang-Hyun Park, Kil To Chong, Eui-Jeon Woo, Hilal Tayara:
NanoBinder: a machine learning assisted nanobody binding prediction tool using Rosetta energy scores. 96 - Nico Domschke, Bruno J. Schmidt, Thomas Gatter, Richard Golnik, Paul Eisenhuth, Fabian Liessmann, Jens Meiler, Peter F. Stadler:
Crossover operators for molecular graphs with an application to virtual drug screening. 97 - Todor Kondic, Anjana Elapavalore, Jessy Krier, Adelene Lai, Hiba Mohammed Taha, Mira Narayanan, Emma L. Schymanski:
Shinyscreen: mass spectrometry data inspection and quality checking utility. 98
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2025-07-23 20:26 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
