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
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 2025-04-09 21:24 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:
