default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDJournal of Cheminformatics, Volume 14
Volume 14, Number 1, December 2022
- Min Wei, Xudong Zhang, Xiaolin Pan
, Bo Wang, Changge Ji, Yifei Qi, John Z. H. Zhang:
HobPre: accurate prediction of human oral bioavailability for small molecules. 1 - Youngchun Kwon, Dongseon Lee, Youn-Suk Choi, Seokho Kang:
Uncertainty-aware prediction of chemical reaction yields with graph neural networks. 2 - Alan Kerstjens, Hans De Winter:
LEADD: Lamarckian evolutionary algorithm for de novo drug design. 3 - Hadar Grimberg, Vinay S. Tiwari, Benjamin Tam, Lihi Gur-Arie, Daniela Gingold, Lea Polachek, Barak Akabayov:
Machine learning approaches to optimize small-molecule inhibitors for RNA targeting. 4 - Ingoo Lee, Hojung Nam:
Sequence-based prediction of protein binding regions and drug-target interactions. 5 - Miao Yu, Georgia Dolios, Lauren Petrick:
Reproducible untargeted metabolomics workflow for exhaustive MS2 data acquisition of MS1 features. 6 - Michael Freitas Gustavo, Toon Verstraelen:
GloMPO (Globally Managed Parallel Optimization): a tool for expensive, black-box optimizations, application to ReaxFF reparameterizations. 7 - Ammar Ammar, Rachel Cavill, Chris T. A. Evelo, Egon L. Willighagen:
PSnpBind: a database of mutated binding site protein-ligand complexes constructed using a multithreaded virtual screening workflow. 8 - Eunyoung Kim, Hojung Nam:
DeSIDE-DDI: interpretable prediction of drug-drug interactions using drug-induced gene expressions. 9 - Arash Keshavarzi Arshadi, Milad Salem, Arash Firouzbakht, Jiann-Shiun Yuan:
MolData, a molecular benchmark for disease and target based machine learning. 10 - Damien E. Coupry, Peter Pogány:
Application of deep metric learning to molecular graph similarity. 11 - Chengyou Liu, Andrew M. Hogan, Hunter Sturm, Mohd Wasif Khan, Md. Mohaiminul Islam, A. S. M. Zisanur Rahman, Rebecca L. Davis, Silvia T. Cardona, Pingzhao Hu:
Deep learning-driven prediction of drug mechanism of action from large-scale chemical-genetic interaction profiles. 12 - David Ferro-Costas, Irea Mosquera-Lois, Antonio Fernández-Ramos:
Correction to: TorsiFlex: an automatic generator of torsional conformers. Application to the twenty proteinogenic amino acids. 13 - Junjie Wang, Naifeng Wen, Chunyu Wang, Lingling Zhao, Liang Cheng:
ELECTRA-DTA: a new compound-protein binding affinity prediction model based on the contextualized sequence encoding. 14 - Min Htoo Lin, Zhengkai Tu, Connor W. Coley:
Improving the performance of models for one-step retrosynthesis through re-ranking. 15 - Yimeng Wang, Yaxin Gu, Chaofeng Lou, Yuning Gong, Zengrui Wu, Weihua Li, Yun Tang, Guixia Liu:
A multitask GNN-based interpretable model for discovery of selective JAK inhibitors. 16 - Melissa F. Adasme, Sarah Naomi Bolz, Ali Al-Fatlawi, Michael Schroeder:
Decomposing compounds enables reconstruction of interaction fingerprints for structure-based drug screening. 17 - Jiazhen He, Eva Nittinger, Christian Tyrchan, Werngard Czechtizky, Atanas Patronov, Esben Jannik Bjerrum, Ola Engkvist:
Transformer-based molecular optimization beyond matched molecular pairs. 18 - Chong Lu, Shien Liu, Weihua Shi, Jun Yu, Zhou Zhou, Xiaoxiao Zhang, Xiaoli Lu, Faji Cai, Ning Xia, Yikai Wang:
Systemic evolutionary chemical space exploration for drug discovery. 19 - Maxime Langevin, Rodolphe Vuilleumier, Marc Bianciotto:
Explaining and avoiding failure modes in goal-directed generation of small molecules. 20 - Christina Humer, Henry Heberle, Floriane Montanari, Thomas Wolf, Florian Huber, Ryan Henderson, Julian Heinrich, Marc Streit:
ChemInformatics Model Explorer (CIME): exploratory analysis of chemical model explanations. 21 - Simon Bray, Tim Dudgeon, Rachael Skyner, Rolf Backofen, Björn A. Grüning, Frank von Delft:
Galaxy workflows for fragment-based virtual screening: a case study on the SARS-CoV-2 main protease. 22 - Ning-Ning Wang, Xiang-Gui Wang, Guo-Li Xiong, Zi-Yi Yang, Ai-Ping Lu, Xiang Chen, Shao Liu, Tingjun Hou, Dong-Sheng Cao:
Machine learning to predict metabolic drug interactions related to cytochrome P450 isozymes. 23 - Brendan D. McKay, Mehmet Aziz Yirik, Christoph Steinbeck:
Surge: a fast open-source chemical graph generator. 24 - Barbara Zdrazil, Rajarshi Guha:
Diversifying cheminformatics. 25 - Huanyu Tao, Qilong Wu, Xuejun Zhao, Peicong Lin, Sheng-You Huang:
Efficient 3D conformer generation of cyclic peptides formed by a disulfide bond. 26 - Doha Naga, Wolfgang Muster, Eunice Musvasva, Gerhard F. Ecker:
Off-targetP ML: an open source machine learning framework for off-target panel safety assessment of small molecules. 27 - Daniela Dolciami, Eloy D. Villasclaras-Fernández, Christos C. Kannas, Mirco Meniconi, Bissan Al-Lazikani, Albert A. Antolin:
canSAR chemistry registration and standardization pipeline. 28 - Diego Garay-Ruiz, Carles Bo:
Chemical reaction network knowledge graphs: the OntoRXN ontology. 29 - Ruihan Zhang, Shoupeng Ren, Qi Dai, Tianze Shen, Xiaoli Li, Jin Li, Weilie Xiao:
InflamNat: web-based database and predictor of anti-inflammatory natural products. 30 - Henning Otto Brinkhaus, Kohulan Rajan, Achim Zielesny, Christoph Steinbeck:
RanDepict: Random chemical structure depiction generator. 31 - Moritz Walter, Luke N. Allen, Antonio de la Vega de León, Samuel J. Webb, Valerie J. Gillet:
Analysis of the benefits of imputation models over traditional QSAR models for toxicity prediction. 32 - Constantino A. García, Alberto Gil-de-la-Fuente, Coral Barbas, Abraham Otero:
Probabilistic metabolite annotation using retention time prediction and meta-learned projections. 33 - Barbara R. Terlouw, Sophie P. J. M. Vromans, Marnix H. Medema:
PIKAChU: a Python-based informatics kit for analysing chemical units. 34 - Kedan He:
Pharmacological affinity fingerprints derived from bioactivity data for the identification of designer drugs. 35 - Henning Otto Brinkhaus, Achim Zielesny, Christoph Steinbeck, Kohulan Rajan:
DECIMER - hand-drawn molecule images dataset. 36 - Barbara Füzi, Rahuman S. Malik-Sheriff, Emma J. Manners, Henning Hermjakob, Gerhard F. Ecker:
KNIME workflow for retrieving causal drug and protein interactions, building networks, and performing topological enrichment analysis demonstrated by a DILI case study. 37 - Peter Willett:
Commentary: the first twelve years of the Journal of chemoinformatics. 38 - Bienfait Kabuyaya Isamura, Kevin A. Lobb:
AMADAR: a python-based package for large scale prediction of Diels-Alder transition state geometries and IRC path analysis. 39 - Maryam Abbasi, Beatriz P. Santos, Tiago C. Pereira, Raul Sofia, Nelson R. C. Monteiro, Carlos J. V. Simões, Rui M. M. Brito, Bernardete Ribeiro, José Luís Oliveira, Joel P. Arrais:
Designing optimized drug candidates with Generative Adversarial Network. 40 - Zhanpeng Xu, Jianhua Li, Zhaopeng Yang, Shiliang Li, Honglin Li:
SwinOCSR: end-to-end optical chemical structure recognition using a Swin Transformer. 41 - Kedan He:
Correction to: Pharmacological affinity fingerprints derived from bioactivity data for the identification of designer drugs. 42 - Yifan Wang, Jake Kalscheur, Elvis Ebikade, Qiang Li, Dionisios G. Vlachos:
LigninGraphs: lignin structure determination with multiscale graph modeling. 43 - Xiaochu Tong, Dingyan Wang, Xiaoyu Ding, Xiaoqin Tan, Qun Ren, Geng Chen, Yu Rong, Tingyang Xu, Junzhou Huang, Hualiang Jiang, Mingyue Zheng, Xutong Li:
Blood-brain barrier penetration prediction enhanced by uncertainty estimation. 44 - Eitan Margulis, Yuli Slavutsky, Tatjana Lang, Maik Behrens, Yuval Benjamini, Masha Y. Niv:
BitterMatch: recommendation systems for matching molecules with bitter taste receptors. 45 - Mengting Huang, Chaofeng Lou, Zengrui Wu, Weihua Li, Philip W. Lee, Yun Tang, Guixia Liu:
In silico prediction of UGT-mediated metabolism in drug-like molecules via graph neural network. 46 - Akshai P. Sreenivasan, Philip J. Harrison, Wesley Schaal, Damian J. Matuszewski, Kim Kultima, Ola Spjuth:
Predicting protein network topology clusters from chemical structure using deep learning. 47 - Neeraj Kumar, Vishal Acharya:
Machine intelligence-driven framework for optimized hit selection in virtual screening. 48 - Nina Lukashina, Elena Kartysheva, Ola Spjuth, Elizaveta Virko, Aleksei Shpilman:
SimVec: predicting polypharmacy side effects for new drugs. 49 - Jeremy R. Ash, Jacqueline M. Hughes-Oliver:
Confidence bands and hypothesis tests for hit enrichment curves. 50 - Norberto Sánchez-Cruz, Emma Schymanski:
Paths to Cheminformatics: Q&A with Norberto Sánchez-Cruz and Emma Schymanski. 51 - Yue Kong, Xiaoman Zhao, Ruizi Liu, Zhenwu Yang, Hongyan Yin, Bowen Zhao, Jinling Wang, Bingjie Qin, Aixia Yan:
Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation. 52 - Maryam Abbasi, Beatriz P. Santos, Tiago C. Pereira, Raul Sofa, Nelson R. C. Monteiro, Carlos J. V. Simões, Rui M. M. Brito, Bernardete Ribeiro, José Luís Oliveira, Joel P. Arrais:
Correction to: Designing optimized drug candidates with Generative Adversarial Network. 53 - Aljosa Smajic, Melanie Grandits, Gerhard F. Ecker:
Using Jupyter Notebooks for re-training machine learning models. 54 - Olga A. Tarasova, Anastasia V. Rudik, Nadezhda Yu. Biziukova, Dmitry Filimonov, Vladimir Poroikov:
Chemical named entity recognition in the texts of scientific publications using the naïve Bayes classifier approach. 55 - Yang Yu, Zhe Wang, Lingling Wang, Sheng Tian, Tingjun Hou, Huiyong Sun:
Predicting the mutation effects of protein-ligand interactions via end-point binding free energy calculations: strategies and analyses. 56 - Jeaphianne P. M. van Rijn, Antreas Afantitis, Mustafa Culha, Maria Dusinska, Thomas E. Exner, Nina Jeliazkova, Eleonora Marta Longhin, Iseult Lynch, Georgia Melagraki, Penny Nymark, Anastasios G. Papadiamantis, David A. Winkler, Hulya Yilmaz, Egon L. Willighagen:
European Registry of Materials: global, unique identifiers for (undisclosed) nanomaterials. 57 - Yuri Kochnev, Jacob D. Durrant:
FPocketWeb: protein pocket hunting in a web browser. 58 - Rubaiyat Mohammad Khondaker, Stephen Gow, Samantha Kanza, Jeremy G. Frey, Mahesan Niranjan:
Robustness under parameter and problem domain alterations of Bayesian optimization methods for chemical reactions. 59 - Xinqiao Wang, Chuansheng Yao, Yun Zhang, Jiahui Yu, Haoran Qiao, Chengyun Zhang, Yejian Wu, Renren Bai, Hongliang Duan:
From theory to experiment: transformer-based generation enables rapid discovery of novel reactions. 60 - Fidan Musazade, Narmin Jamalova, Jamaladdin Hasanov:
Review of techniques and models used in optical chemical structure recognition in images and scanned documents. 61 - Milka Ljoncheva, Tomaz Stepisnik, Tina Kosjek, Saso Dzeroski:
Machine learning for identification of silylated derivatives from mass spectra. 62 - Stefan Müller, Christoph Flamm, Peter F. Stadler:
What makes a reaction network "chemical"? 63 - Yasemin Yesiltepe, Niranjan Govind, Thomas O. Metz, Ryan S. Renslow:
An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts. 64 - Mohamed-Amine Chadi, Hajar Mousannif, Ahmed Aamouche:
Conditional reduction of the loss value versus reinforcement learning for biassing a de-novo drug design generator. 65 - Jan C. Brammer, Gerd Blanke, Claudia Kellner, Alexander Hoffmann, Sonja Herres-Pawlis, Ulrich Schatzschneider:
TUCAN: A molecular identifier and descriptor applicable to the whole periodic table from hydrogen to oganesson. 66 - Sangjin Ahn, Si Eun Lee, Mi-Hyun Kim:
Random-forest model for drug-target interaction prediction via Kullbeck-Leibler divergence. 67 - Morgan C. Thomas, Noel M. O'Boyle, Andreas Bender, Chris de Graaf:
Augmented Hill-Climb increases reinforcement learning efficiency for language-based de novo molecule generation. 68 - Ani Tevosyan, Lusine Khondkaryan, Hrant Khachatrian, Gohar Tadevosyan, Lilit Apresyan, Nelly Babayan, Helga Stopper, Zaven Navoyan:
Improving VAE based molecular representations for compound property prediction. 69 - Jong Youl Choi, Pei Zhang, Kshitij Mehta, Andrew E. Blanchard, Massimiliano Lupo Pasini:
Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules. 70 - Naifeng Wen, Guanqun Liu, Jie Zhang, Rubo Zhang, Yating Fu, Xu Han:
A fingerprints based molecular property prediction method using the BERT model. 71 - Louis Plyer, Gilles Marcou, Céline Perves, Rachel Schurhammer, Alexandre Varnek:
Implementation of a soft grading system for chemistry in a Moodle plugin. 72 - António J. Preto, Paulo C. Correia, Irina S. Moreira:
DrugTax: package for drug taxonomy identification and explainable feature extraction. 73 - Luca Chiesa, Esther Kellenberger:
One class classification for the detection of β2 adrenergic receptor agonists using single-ligand dynamic interaction data. 74 - Gaoang Wang, Jiahui Yu, Hongyan Du, Chao Shen, Xujun Zhang, Yifei Liu, Yangyang Zhang, Dong-Sheng Cao, Peichen Pan, Tingjun Hou:
VGSC-DB: an online database of voltage-gated sodium channels. 75 - Sangjin Ahn, Si Eun Lee, Mi-Hyun Kim:
Correction : Random-forest model for drug-target interaction prediction via Kullback-Leibler divergence. 76 - Davide Bonanni, Luca Pinzi, Giulio Rastelli:
Development of machine learning classifiers to predict compound activity on prostate cancer cell lines. 77 - Sherif Abdulkader Tawfik, Salvy P. Russo:
Naturally-meaningful and efficient descriptors: machine learning of material properties based on robust one-shot ab initio descriptors. 78 - Jonas Schaub, Julian Zander, Achim Zielesny, Christoph Steinbeck:
Scaffold Generator: a Java library implementing molecular scaffold functionalities in the Chemistry Development Kit (CDK). 79 - Davide Boldini, Lukas Friedrich, Daniel Kuhn, Stephan A. Sieber:
Tuning gradient boosting for imbalanced bioassay modelling with custom loss functions. 80 - Shenggeng Lin, Weizhi Chen, Gengwang Chen, Songchi Zhou, Dong-Qing Wei, Yi Xiong:
MDDI-SCL: predicting multi-type drug-drug interactions via supervised contrastive learning. 81 - Jürgen Bajorath, Ana L. Chávez-Hernández, Miquel Duran-Frigola, Eli Fernández-de Gortari, Johann Gasteiger, Edgar López-López, Gerald M. Maggiora, José L. Medina-Franco, Oscar Méndez-Lucio, Jordi Mestres, Ramón Alain Miranda-Quintana, Tudor I. Oprea, Fabien Plisson, Fernando D. Prieto-Martínez, Raquel Rodríguez-Pérez, Paola Rondón-Villarreal, Fernanda I. Saldívar-González, Norberto Sánchez-Cruz, Marilia Valli:
Chemoinformatics and artificial intelligence colloquium: progress and challenges in developing bioactive compounds. 82 - Hwanhee Kim, Soohyun Ko, Byung Ju Kim, Sung Jin Ryu, Jaegyoon Ahn:
Predicting chemical structure using reinforcement learning with a stack-augmented conditional variational autoencoder. 83 - Mingyang Wang, Jike Wang, Gaoqi Weng, Yu Kang, Peichen Pan, Dan Li, Yafeng Deng, Honglin Li, Chang-Yu Hsieh, Tingjun Hou:
ReMODE: a deep learning-based web server for target-specific drug design. 84 - Adelene Lai, Jonas Schaub, Christoph Steinbeck, Emma Schymanski:
An algorithm to classify homologous series within compound datasets. 85 - Iiris Sundin, Alexey Voronov, Haoping Xiao, Kostas Papadopoulos, Esben Jannik Bjerrum, Markus Heinonen, Atanas Patronov, Samuel Kaski, Ola Engkvist:
Human-in-the-loop assisted de novo molecular design. 86 - Vincent F. Scalfani, Vishank D. Patel, Avery M. Fernandez:
Visualizing chemical space networks with RDKit and NetworkX. 87 - Xiaofan Zheng, Yoichi Tomiura, Kenshi Hayashi:
Investigation of the structure-odor relationship using a Transformer model. 88 - Liu-Xia Zhang, Jie Dong, Hui Wei, Shao-Hua Shi, Ai-Ping Lu, Gui-Ming Deng, Dong-Sheng Cao:
TCMSID: a simplified integrated database for drug discovery from traditional chinese medicine. 89
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 2024-12-10 20:34 CET 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:
