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ORCIDJournal of Cheminformatics, Volume 16
Volume 16, Number 1, December 2024
- Soyeon Lee, Sunyong Yoo
:
InterDILI: interpretable prediction of drug-induced liver injury through permutation feature importance and attention mechanism. 1 - Debby D. Wang, Wenhui Wu, Ran Wang:
Structure-based, deep-learning models for protein-ligand binding affinity prediction. 2 - Lukasz Maziarka, Dawid Majchrowski, Tomasz Danel, Piotr Gainski, Jacek Tabor, Igor T. Podolak, Pawel M. Morkisz, Stanislaw Jastrzebski:
Relative molecule self-attention transformer. 3 - Yaxin Gu, Yimeng Wang, Keyun Zhu, Weihua Li, Guixia Liu, Yun Tang:
DBPP-Predictor: a novel strategy for prediction of chemical drug-likeness based on property profiles. 4 - Sadettin Y. Ugurlu, David W. McDonald, Huangshu Lei, Alan M. Jones, Shu Li, Henry H. Y. Tong, Mark S. Butler, Shan He:
Cobdock: an accurate and practical machine learning-based consensus blind docking method. 5 - Barbara Zdrazil, Rajarshi Guha, Karina Martínez-Mayorga, Nina Jeliazkova:
Are new ideas harder to find? A note on incremental research and Journal of Cheminformatics' Scientific Contribution Statement. 6 - Tinghao Zhang, Shaohua Sun, Runzhou Wang, Ting Li, Bicheng Gan, Yuezhou Zhang:
BioisoIdentifier: an online free tool to investigate local structural replacements from PDB. 7 - Sadjad Fakouri Baygi, Dinesh Kumar Barupal:
IDSL_MINT: a deep learning framework to predict molecular fingerprints from mass spectra. 8 - Paula Carracedo-Reboredo, Eider Aranzamendi, Shan He, Sonia Arrasate, Cristian R. Munteanu, Carlos Fernandez-Lozano, Nuria Sotomayor, Esther Lete, Humberto González Díaz:
MATEO: intermolecular α-amidoalkylation theoretical enantioselectivity optimization. Online tool for selection and design of chiral catalysts and products. 9 - Wei-Cheng Huang, Wei-Ting Lin, Ming-Shiu Hung, Jinq-Chyi Lee, Chun-Wei Tung:
Decrypting orphan GPCR drug discovery via multitask learning. 10 - Lung-Yi Chen, Yi-Pei Li:
Enhancing chemical synthesis: a two-stage deep neural network for predicting feasible reaction conditions. 11 - Anuj Gahlawat, Anjali Singh, Hardeep Sandhu, Prabha Garg:
CRAFT: a web-integrated cavity prediction tool based on flow transfer algorithm. 12 - Jiangxia Wu, Yihao Chen, Jingxing Wu, Duancheng Zhao, Jindi Huang, MuJie Lin, Ling Wang:
Large-scale comparison of machine learning methods for profiling prediction of kinase inhibitors. 13 - Jonghyun Lee, Dae Won Jun, Ildae Song, Yun Kim:
DLM-DTI: a dual language model for the prediction of drug-target interaction with hint-based learning. 14 - Derek Long, Liam Eade, Matthew P. Sullivan, Katharina Dost, Samuel M. Meier-Menches, David C. Goldstone, Christian G. Hartinger, Jörg S. Wicker, Katerina Taskova:
AdductHunter: identifying protein-metal complex adducts in mass spectra. 15 - Alexander S. Behr, Hendrik Borgelt, Norbert Kockmann:
Ontologies4Cat: investigating the landscape of ontologies for catalysis research data management. 16 - Kamel Mansouri, José T. Moreira-Filho, Charles N. Lowe, Nathaniel Charest, Todd Martin, Valery Tkachenko, Richard S. Judson, Mike Conway, Nicole C. Kleinstreuer, Antony J. Williams:
Free and open-source QSAR-ready workflow for automated standardization of chemical structures in support of QSAR modeling. 19 - Candida Manelfi, Valerio Tazzari, Filippo Lunghini, Carmen Cerchia, Anna Fava, Alessandro Pedretti, Pieter F. W. Stouten, Giulio Vistoli, Andrea Rosario Beccari:
"DompeKeys": a set of novel substructure-based descriptors for efficient chemical space mapping, development and structural interpretation of machine learning models, and indexing of large databases. 21 - Runhan Shi, Gufeng Yu, Xiaohong Huo, Yang Yang:
Prediction of chemical reaction yields with large-scale multi-view pre-training. 22 - Olivier Beyens, Hans De Winter:
Preventing lipophilic aggregation in cosolvent molecular dynamics simulations with hydrophobic probes using Plumed Automatic Restraining Tool (PART). 23 - Adrià Cereto-Massagué, Santiago Garcia-Vallvé, Gerard Pujadas:
Correction: DecoyFinder, a tool for finding decoy molecules. 24 - Jongmin Han, Youngchun Kwon, Youn-Suk Choi, Seokho Kang:
Improving chemical reaction yield prediction using pre-trained graph neural networks. 25 - Marie Oestreich, Iva Ewert, Matthias Becker:
Small molecule autoencoders: architecture engineering to optimize latent space utility and sustainability. 26 - Karina Beatriz Jimenes Vargas, Alejandro Pazos, Cristian R. Munteanu, Yunierkis Pérez-Castillo, Eduardo Tejera:
Prediction of compound-target interaction using several artificial intelligence algorithms and comparison with a consensus-based strategy. 27 - Sabrina Jaeger-Honz, Karsten Klein, Falk Schreiber:
Systematic analysis, aggregation and visualisation of interaction fingerprints for molecular dynamics simulation data. 28 - Bowen Tang, Zhangming Niu, Xiaofeng Wang, Junjie Huang, Chao Ma, Jing Peng, Yinghui Jiang, Ruiquan Ge, Hongyu Hu, Luhao Lin, Guang Yang:
Automated molecular structure segmentation from documents using ChemSAM. 29 - Tsuyoshi Esaki, Tomoki Yonezawa, Kazuyoshi Ikeda:
A new workflow for the effective curation of membrane permeability data from open ADME information. 30 - Alex K. Chew, Matthew Sender, Zachary Kaplan, Anand Chandrasekaran, Jackson Chief Elk, Andrea R. Browning, H. Shaun Kwak, Mathew D. Halls, Mohammad Atif Faiz Afzal:
Advancing material property prediction: using physics-informed machine learning models for viscosity. 31 - Anna Carbery, Martin Buttenschoen, Rachael Skyner, Frank von Delft, Charlotte M. Deane:
Learnt representations of proteins can be used for accurate prediction of small molecule binding sites on experimentally determined and predicted protein structures. 32 - Lingling Shen, Jian Fang, Lulu Liu, Fei Yang, Jeremy L. Jenkins, Peter S. Kutchukian, He Wang:
Pocket Crafter: a 3D generative modeling based workflow for the rapid generation of hit molecules in drug discovery. 33 - Matteo Krüger, Ashmi Mishra, Peter Spichtinger, Ulrich Pöschl, Thomas Berkemeier:
A numerical compass for experiment design in chemical kinetics and molecular property estimation. 34 - Davide Boldini, Davide Ballabio, Viviana Consonni, Roberto Todeschini, Francesca Grisoni, Stephan A. Sieber:
Effectiveness of molecular fingerprints for exploring the chemical space of natural products. 35 - Thomas E. Lockwood, Alexander Angeloski:
DGet! An open source deuteration calculator for mass spectrometry data. 36 - Maarten R. Dobbelaere, István Lengyel, Christian V. Stevens, Kevin M. Van Geem:
Rxn-INSIGHT: fast chemical reaction analysis using bond-electron matrices. 37 - Wenjia Qian, Xiaorui Wang, Yu Kang, Peichen Pan, Tingjun Hou, Chang-Yu Hsieh:
A general model for predicting enzyme functions based on enzymatic reactions. 38 - Peter B. R. Hartog, Fabian Krüger, Samuel Genheden, Igor V. Tetko:
Using test-time augmentation to investigate explainable AI: inconsistencies between method, model and human intuition. 39 - Klaudia Caba, Viet-Khoa Tran-Nguyen, Taufiq Rahman, Pedro J. Ballester:
Comprehensive machine learning boosts structure-based virtual screening for PARP1 inhibitors. 40 - Xinwei Zhao, Junqing Xu, Youyuan Shui, Mengdie Xu, Jie Hu, Xiaoyan Liu, Kai Che, Junjie Wang, Yun Liu:
PermuteDDS: a permutable feature fusion network for drug-drug synergy prediction. 41 - Michael Blakey, Samantha Kanza, Jeremy G. Frey:
Zombie cheminformatics: extraction and conversion of Wiswesser Line Notation (WLN) from chemical documents. 42 - Sébastien J. J. Guesné, Thierry Hanser, Stéphane Werner, Samuel Boobier, Shaylyn Scott:
Mind your prevalence! 43 - Ming Du, Xingran Xie, Jing Luo, Jin Li:
Meta-learning-based Inductive logistic matrix completion for prediction of kinase inhibitors. 44 - Satnam Singh, Gina Zeh, Jessica Freiherr, Thilo Bauer, Isik Türkmen, Andreas Grasskamp:
Classification of substances by health hazard using deep neural networks and molecular electron densities. 45 - Maryam Astero, Juho Rousu:
Learning symmetry-aware atom mapping in chemical reactions through deep graph matching. 46 - Zixin Zhuang, Amanda S. Barnard:
Classification of battery compounds using structure-free Mendeleev encodings. 47 - Zhijiang Yang, Tengxin Huang, Li Pan, Jingjing Wang, Liangliang Wang, Junjie Ding, Junhua Xiao:
QuanDB: a quantum chemical property database towards enhancing 3D molecular representation learning. 48 - Jeaphianne P. M. van Rijn, Marvin Martens, Ammar Ammar, Mihaela Roxana Cimpan, Valerie Fessard, Peter Hoet, Nina Jeliazkova, Sivakumar Murugadoss, Ivana Vinkovic Vrcek, Egon L. Willighagen:
From papers to RDF-based integration of physicochemical data and adverse outcome pathways for nanomaterials. 49 - Nomagugu B. Ncube, Matshawandile Tukulula, Krishna G. Govender:
Leveraging computational tools to combat malaria: assessment and development of new therapeutics. 50 - Christina Humer, Rachel Nicholls, Henry Heberle, Moritz Heckmann, Michael Pühringer, Thomas Wolf, Maximilian Lübbesmeyer, Julian Heinrich, Julius Hillenbrand, Giulio Volpin, Marc Streit:
CIME4R: Exploring iterative, AI-guided chemical reaction optimization campaigns in their parameter space. 51 - Julia Rahman, M. A. Hakim Newton, Mohammed Eunus Ali, Abdul Sattar:
Distance plus attention for binding affinity prediction. 52 - Markus Orsi, Jean-Louis Reymond:
One chiral fingerprint to find them all. 53 - Hunter N. B. Moseley, Philippe Rocca-Serra, Reza M. Salek, Masanori Arita, Emma Schymanski:
InChI isotopologue and isotopomer specifications. 54 - Hengwei Chen, Jürgen Bajorath:
Generative design of compounds with desired potency from target protein sequences using a multimodal biochemical language model. 55 - Zachary A. Rollins, Alan C. Cheng, Essam Metwally:
MolPROP: Molecular Property prediction with multimodal language and graph fusion. 56 - Lakshidaa Saigiridharan, Alan Kai Hassen, Helen Lai, Paula Torren-Peraire, Ola Engkvist, Samuel Genheden:
AiZynthFinder 4.0: developments based on learnings from 3 years of industrial application. 57 - David Meijer, Marnix H. Medema, Justin J. J. van der Hooft:
CineMol: a programmatically accessible direct-to-SVG 3D small molecule drawer. 58 - Hocheol Lim:
Development of scoring-assisted generative exploration (SAGE) and its application to dual inhibitor design for acetylcholinesterase and monoamine oxidase B. 59 - Joseph Heeley, Samuel Boobier, Jonathan D. Hirst:
Solvent flashcards: a visualisation tool for sustainable chemistry. 60 - Danh Bui Thi, Youzhong Liu, Jennifer L. Lippens, Kris Laukens, Thomas De Vijlder:
TransExION: a transformer based explainable similarity metric for comparing IONS in tandem mass spectrometry. 61 - Said Moshawih, Zhen Hui Bu, Hui Poh Goh, Nurolaini Kifli, Lam Hong Lee, Khang Wen Goh, Chiau Ming Long:
Consensus holistic virtual screening for drug discovery: a novel machine learning model approach. 62 - Reagan M. Mogire, Silviane A. Miruka, Dennis W. Juma, Case W. McNamara, Ben Andagalu, Jeremy N. Burrows, Elodie Chenu, James Duffy, Bernhards Ogutu, Hoseah M. Akala:
Protein target similarity is positive predictor of in vitro antipathogenic activity: a drug repurposing strategy for Plasmodium falciparum. 63 - Morgan C. Thomas, Noel M. O'Boyle, Andreas Bender, Chris de Graaf:
MolScore: a scoring, evaluation and benchmarking framework for generative models in de novo drug design. 64 - Trevor N. Brown, Alessandro Sangion, Jon A. Arnot:
Identifying uncertainty in physical-chemical property estimation with IFSQSAR. 65 - Jeevan Kandel, Palistha Shrestha, Hilal Tayara, Kil To Chong:
PUResNetV2.0: a deep learning model leveraging sparse representation for improved ligand binding site prediction. 66 - Yufang Zhang, Jiayi Li, Shenggeng Lin, Jianwei Zhao, Yi Xiong, Dong-Qing Wei:
An end-to-end method for predicting compound-protein interactions based on simplified homogeneous graph convolutional network and pre-trained language model. 67 - Zhijiang Yang, Tengxin Huang, Li Pan, Jingjing Wang, Liangliang Wang, Junjie Ding, Junhua Xiao:
Correction: QuanDB: a quantum chemical property database towards enhancing 3D molecular representation learning. 68 - Sunghwan Kim, Bo Yu, Qingliang Li, Evan E. Bolton:
PubChem synonym filtering process using crowdsourcing. 69 - Arnau Comajuncosa-Creus, Aksel Lenes, Miguel Sánchez-Palomino, Dylan Dalton, Patrick Aloy:
Stereochemically-aware bioactivity descriptors for uncharacterized chemical compounds. 70 - Xiaofan Zheng, Yoichi Tomiura:
A BERT-based pretraining model for extracting molecular structural information from a SMILES sequence. 71 - Elena Bandini, Rodrigo Castellano Ontiveros, Ardiana Kajtazi, Hamed Eghbali, Frédéric Lynen:
Physicochemical modelling of the retention mechanism of temperature-responsive polymeric columns for HPLC through machine learning algorithms. 72 - Niklas Dobberstein, Astrid Maass, Jan Hamaekers:
Llamol: a dynamic multi-conditional generative transformer for de novo molecular design. 73 - Lung-Yi Chen, Yi-Pei Li:
AutoTemplate: enhancing chemical reaction datasets for machine learning applications in organic chemistry. 74 - Staffan Arvidsson McShane, Ulf Norinder, Jonathan Alvarsson, Ernst Ahlberg, Lars Carlsson, Ola Spjuth:
CPSign: conformal prediction for cheminformatics modeling. 75 - Zihui Huang, Liqiang He, Yuhang Yang, Andi Li, Zhiwen Zhang, Siwei Wu, Yang Wang, Yan He, Xujie Liu:
Application of machine reading comprehension techniques for named entity recognition in materials science. 76 - Morgan Thomas, Mazen Ahmad, Gary Tresadern, Gianni De Fabritiis:
PromptSMILES: prompting for scaffold decoration and fragment linking in chemical language models. 77 - Kohulan Rajan, Henning Otto Brinkhaus, Achim Zielesny, Christoph Steinbeck:
Advancements in hand-drawn chemical structure recognition through an enhanced DECIMER architecture. 78 - Ruifeng Zhou, Jing Fan, Sishu Li, Wenjie Zeng, Yilun Chen, Xiaoshan Zheng, Hongyang Chen, Jun Liao:
LVPocket: integrated 3D global-local information to protein binding pockets prediction with transfer learning of protein structure classification. 79 - Kaipeng Zeng, Bo Yang, Xin Zhao, Yu Zhang, Fan Nie, Xiaokang Yang, Yaohui Jin, Yanyan Xu:
Ualign: pushing the limit of template-free retrosynthesis prediction with unsupervised SMILES alignment. 80 - Raghad Al-Jarf, Carlos H. M. Rodrigues, Yoochan Myung, Douglas E. V. Pires, David B. Ascher:
piscesCSM: prediction of anticancer synergistic drug combinations. 81 - Tieu-Long Phan, Klaus Weinbauer, Thomas Gärtner, Daniel Merkle, Jakob L. Andersen, Rolf Fagerberg, Peter F. Stadler:
Reaction rebalancing: a novel approach to curating reaction databases. 82 - Shuan Chen, Yousung Jung:
Estimating the synthetic accessibility of molecules with building block and reaction-aware SAScore. 83 - Karla P. Godinez-Macias, Elizabeth A. Winzeler:
CACTI: an in silico chemical analysis tool through the integration of chemogenomic data and clustering analysis. 84 - Vishal Dey, Xia Ning:
Enhancing molecular property prediction with auxiliary learning and task-specific adaptation. 85 - Rayyan T. Khan, Petra Pokorna, Jan Stourac, Simeon Borko, Ihor Arefiev, Joan Planas-Iglesias, Adam Dobias, Gaspar R. P. Pinto, Veronika Szotkowska, Jaroslav Sterba, Ondrej Slaby, Jirí Damborský, Stanislav Mazurenko, David Bednar:
A computational workflow for analysis of missense mutations in precision oncology. 86 - Gergely Zahoránszky-Köhalmi, Kanny K. Wan, Alexander G. Godfrey:
Hilbert-curve assisted structure embedding method. 87 - Niek F. de Jonge, Helge Hecht, Michael Strobel, Mingxun Wang, Justin J. J. van der Hooft, Florian Huber:
Reproducible MS/MS library cleaning pipeline in matchms. 88 - Chengwei Zhang, Yushuang Zhai, Ziyang Gong, Hongliang Duan, Yuan-Bin She, Yun-Fang Yang, An Su:
Transfer learning across different chemical domains: virtual screening of organic materials with deep learning models pretrained on small molecule and chemical reaction data. 89 - Louis Plyer, Gilles Marcou, Céline Perves, Fanny Bonachéra, Alexandre Varnek:
Implementation of a soft grading system for chemistry in a Moodle plugin: reaction handling. 90 - Run-Hsin Lin, Pinpin Lin, Chia-Chi Wang, Chun-Wei Tung:
A novel multitask learning algorithm for tasks with distinct chemical space: zebrafish toxicity prediction as an example. 91 - Yang Tan, Mingchen Li, Ziyi Zhou, Pan Tan, Huiqun Yu, Guisheng Fan, Liang Hong:
PETA: evaluating the impact of protein transfer learning with sub-word tokenization on downstream applications. 92 - Jasmin Hafner, Tim Lorsbach, Sebastian Schmidt, Liam Brydon, Katharina Dost, Kunyang Zhang, Kathrin Fenner, Jörg Wicker:
Advancements in biotransformation pathway prediction: enhancements, datasets, and novel functionalities in enviPath. 93 - Xiuyuan Hu, Guoqing Liu, Quanming Yao, Yang Zhao, Hao Zhang:
Hamiltonian diversity: effectively measuring molecular diversity by shortest Hamiltonian circuits. 94 - Jiazhen He, Alessandro Tibo, Jon Paul Janet, Eva Nittinger, Christian Tyrchan, Werngard Czechtizky, Ola Engkvist:
Evaluation of reinforcement learning in transformer-based molecular design. 95 - Felix Bänsch, Mirco Daniel, Harald Lanig, Christoph Steinbeck, Achim Zielesny:
An automated calculation pipeline for differential pair interaction energies with molecular force fields using the Tinker Molecular Modeling Package. 96 - Paola Moyano-Gómez, Jukka V. Lehtonen, Olli T. Pentikäinen, Pekka A. Postila:
Building shape-focused pharmacophore models for effective docking screening. 97 - Sergey Sosnin:
MolCompass: multi-tool for the navigation in chemical space and visual validation of QSAR/QSPR models. 98 - Maarten R. Dobbelaere, István Lengyel, Christian V. Stevens, Kevin M. Van Geem:
Geometric deep learning for molecular property predictions with chemical accuracy across chemical space. 99 - Janosch Menke, Yasmine Nahal, Esben Jannik Bjerrum, Mikhail Kabeshov, Samuel Kaski, Ola Engkvist:
Metis: a python-based user interface to collect expert feedback for generative chemistry models. 100 - José T. Moreira-Filho, Dhruv Ranganath, Mike Conway, Charles Schmitt, Nicole C. Kleinstreuer, Kamel Mansouri:
Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow. 101 - Fiona C. Y. Yu, Jorge L. Galvez Vallejo, Giuseppe M. J. Barca:
Automatic molecular fragmentation by evolutionary optimisation. 102 - Yuto Ohnuki, Manato Akiyama, Yasubumi Sakakibara:
Deep learning of multimodal networks with topological regularization for drug repositioning. 103 - Noah Kleinschmidt, Thomas Lemmin:
BuildAMol: a versatile Python toolkit for fragment-based molecular design. 104 - Chloe Engler Hart, António J. Preto, Shaurya Chanana, David Healey, Tobias Kind, Daniel Domingo-Fernández:
Evaluating the generalizability of graph neural networks for predicting collision cross section. 105 - Barbara R. Terlouw, Friederike Biermann, Sophie P. J. M. Vromans, Elham Zamani, Eric J. N. Helfrich, Marnix H. Medema:
RAIChU: automating the visualisation of natural product biosynthesis. 106 - Zhiguang Fan, Yuedong Yang, Mingyuan Xu, Hongming Chen:
EC-Conf: A ultra-fast diffusion model for molecular conformation generation with equivariant consistency. 107 - Sven Marcel Stefan, Katja Stefan, Vigneshwaran Namasivayam:
Computer-aided pattern scoring (C@PS): a novel cheminformatic workflow to predict ligands with rare modes-of-action. 108 - Luis H. M. Torres, Joel P. Arrais, Bernardete Ribeiro:
Combining graph neural networks and transformers for few-shot nuclear receptor binding activity prediction. 109 - Samar Monem, Aboul Ella Hassanien, Alaa H. Abdel-Hamid:
A multi-view feature representation for predicting drugs combination synergy based on ensemble and multi-task attention models. 110 - Prashant Srivastava, Alexandra Steuer, Francesco Ferri, Alessandro Nicoli, Kristian Schultz, Saptarshi Bej, Antonella Di Pizio, Olaf Wolkenhauer:
Bitter peptide prediction using graph neural networks. 111 - Sejal Sharma, Liping Feng, Nicha Boonpattrawong, Arvinder Kapur, Lisa Barroilhet, Manish S. Patankar, Spencer S. Ericksen:
Data mining of PubChem bioassay records reveals diverse OXPHOS inhibitory chemotypes as potential therapeutic agents against ovarian cancer. 112 - Yuting Liu, Akiyasu C. Yoshizawa, Yiwei Ling, Shujiro Okuda:
Insights into predicting small molecule retention times in liquid chromatography using deep learning. 113 - Ondrej Vavra, Jonathan D. Tyzack, Farzan Haddadi, Jan Stourac, Jirí Damborský, Stanislav Mazurenko, Janet M. Thornton, David Bednar:
Large-scale annotation of biochemically relevant pockets and tunnels in cognate enzyme-ligand complexes. 114 - Miguel García-Ortegón, Srijit Seal, Carl Rasmussen, Andreas Bender, Sergio Bacallado:
Graph neural processes for molecules: an evaluation on docking scores and strategies to improve generalization. 115 - Sadettin Y. Ugurlu, David W. McDonald, Shan He:
MEF-AlloSite: an accurate and robust Multimodel Ensemble Feature selection for the Allosteric Site identification model. 116 - Zeqing Bao, Gary Tom, Austin H. Cheng, Jeffrey Watchorn, Alán Aspuru-Guzik, Christine Allen:
Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning. 117 - Sina Abdollahi, Darius P. Schaub, Madalena Barroso, Nora C. Laubach, Wiebke Hutwelker, Ulf Panzer, S. øren W. Gersting, Stefan Bonn:
A comprehensive comparison of deep learning-based compound-target interaction prediction models to unveil guiding design principles. 118 - Candra Zonyfar, Soualihou Ngnamsie Njimbouom, Sophia Mosalla, Jeong-Dong Kim:
GTransCYPs: an improved graph transformer neural network with attention pooling for reliably predicting CYP450 inhibitors. 119 - Peter Willett:
Searching chemical databases in the pre-history of cheminformatics. 120 - Yiyu Hong, Junsu Ha, Jaemin Sim, Chae Jo Lim, Kwang-Seok Oh, Ramakrishnan Chandrasekaran, Bomin Kim, Jieun Choi, Junsu Ko, Woong-Hee Shin, Juyong Lee:
Accurate prediction of protein-ligand interactions by combining physical energy functions and graph-neural networks. 121 - Domenico Gadaleta, Marina Garcia de Lomana, Eva Serrano-Candelas, Rita Ortega-Vallbona, Rafael Gozalbes, Alessandra Roncaglioni, Emilio Benfenati:
Quantitative structure-activity relationships of chemical bioactivity toward proteins associated with molecular initiating events of organ-specific toxicity. 122 - Aleksandra Ivanova, Olena Mokshyna, Pavel G. Polishchuk:
StreaMD: the toolkit for high-throughput molecular dynamics simulations. 123 - Jürgen Bajorath:
Milestones in chemoinformatics: global view of the field. 124 - Jue Wang, Yufan Liu, Boxue Tian:
Protein-small molecule binding site prediction based on a pre-trained protein language model with contrastive learning. 125 - Javier S. Utgés, Geoffrey J. Barton:
Comparative evaluation of methods for the prediction of protein-ligand binding sites. 126 - Gintautas Kamuntavicius, Alvaro Prat, Tanya Paquet, Orestis Bastas, Hisham Abdel-Aty, Qing Sun, Carsten B. Andersen, John Harman, Marc E. Siladi, Daniel R. Rines, Sarah J. L. Flatters, Roy Tal, Povilas Norvaisas:
Accelerated hit identification with target evaluation, deep learning and automated labs: prospective validation in IRAK1. 127 - Helle W. van den Maagdenberg, Martin Sícho, David Alencar Araripe, Sohvi Luukkonen, Linde Schoenmaker, Michiel Jespers, Olivier J. M. Béquignon, Marina Gorostiola González, Remco L. van den Broek, Andrius Bernatavicius, J. G. Coen van Hasselt, Piet H. Van Der Graaf, Gerard J. P. van Westen:
QSPRpred: a Flexible Open-Source Quantitative Structure-Property Relationship Modelling Tool. 128 - Hector Flores-Hernandez, Emmanuel Martinez-Ledesma:
A systematic review of deep learning chemical language models in recent era. 129 - Manuel González Lastre, Pablo Pou, Miguel Wiche, Daniel Ebeling, Andre Schirmeisen, Rubén Pérez:
Molecular identification via molecular fingerprint extraction from atomic force microscopy images. 130 - Sarveswara Rao Vangala, Sowmya Ramaswamy Krishnan, Navneet Bung, Dhandapani Nandagopal, Gomathi Ramasamy, Satyam Kumar, Sridharan Sankaran, Rajgopal Srinivasan, Arijit Roy:
Suitability of large language models for extraction of high-quality chemical reaction dataset from patent literature. 131 - Haochen Chen, Tao Liang, Kai Tan, Anan Wu, Xin Lu:
GT-NMR: a novel graph transformer-based approach for accurate prediction of NMR chemical shifts. 132 - Piao-Yang Cao, Yang He, Mingyang Cui, Xiao-Min Zhang, Qingye Zhang, Hong-Yu Zhang:
Group graph: a molecular graph representation with enhanced performance, efficiency and interpretability. 133 - Emna Harigua-Souiai, Ons Masmoudi, Samer Makni, Rafeh Oualha, Yosser Z. Abdelkrim, Sara Hamdi, Oussama Souiai, Ikram Guizani:
cidalsDB: an AI-empowered platform for anti-pathogen therapeutics research. 134 - Markus Dablander, Thierry Hanser, Renaud Lambiotte, Garrett M. Morris:
Sort & Slice: a simple and superior alternative to hash-based folding for extended-connectivity fingerprints. 135 - João Correia, João Capela, Miguel Rocha:
Deepmol: an automated machine and deep learning framework for computational chemistry. 136 - Hakjean Kim, Seongok Ryu, Nuri Jung, Jinsol Yang, Chaok Seok:
CSearch: chemical space search via virtual synthesis and global optimization. 137 - Yasmine Nahal, Janosch Menke, Julien Martinelli, Markus Heinonen, Mikhail Kabeshov, Jon Paul Janet, Eva Nittinger, Ola Engkvist, Samuel Kaski:
Human-in-the-loop active learning for goal-oriented molecule generation. 138 - Igor V. Tetko, Ruud van Deursen, Guillaume Godin:
Be aware of overfitting by hyperparameter optimization! 139 - Fabio Herrera-Rocha, Miguel Fernández-Niño, Jorge Duitama, Mónica P. Cala, María José Chica, Ludger A. Wessjohann, Mehdi D. Davari, Andrés Fernando González Barrios:
FlavorMiner: a machine learning platform for extracting molecular flavor profiles from structural data. 140 - Yufan Chen, Ching Ting Leung, Yong Huang, Jianwei Sun, Hao Chen, Hanyu Gao:
MolNexTR: a generalized deep learning model for molecular image recognition. 141 - Jianmin Wang, Jiashun Mao, Chunyan Li, Hongxin Xiang, Xun Wang, Shuang Wang, Zixu Wang, Yangyang Chen, Yuquan Li, Kyoung Tai No, Tao Song, Xiangxiang Zeng:
Interface-aware molecular generative framework for protein-protein interaction modulators. 142 - Tianbiao Yang, Xiaoyu Ding, Elizabeth McMichael, Frank W. Pun, Alex Aliper, Feng Ren, Alex Zhavoronkov, Xiao Ding:
AttenhERG: a reliable and interpretable graph neural network framework for predicting hERG channel blockers. 143 - Aleksandra Ivanova, Olena Mokshyna, Pavel G. Polishchuk:
Correction: StreaMD: the toolkit for high-throughput molecular dynamics simulations. 144 - Domenico Gadaleta, Eva Serrano-Candelas, Rita Ortega-Vallbona, Erika Colombo, Marina Garcia de Lomana, Giada Biava, Pablo Aparicio-Sánchez, Alessandra Roncaglioni, Rafael Gozalbes, Emilio Benfenati:
Comprehensive benchmarking of computational tools for predicting toxicokinetic and physicochemical properties of chemicals. 145 - Kohulan Rajan, Achim Zielesny, Christoph Steinbeck:
STOUT V2.0: SMILES to IUPAC name conversion using transformer models. 146 - Guishen Wang, Hangchen Zhang, Mengting Shao, Yuncong Feng, Chen Cao, Xiaowen Hu:
DeepTGIN: a novel hybrid multimodal approach using transformers and graph isomorphism networks for protein-ligand binding affinity prediction. 147
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