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ORCIDJournal of Cheminformatics, Volume 15
Volume 15, Number 1, December 2023
- Felix Bänsch
, Jonas Schaub, Betül Sevindik, Samuel Behr, Julian Zander, Christoph Steinbeck, Achim Zielesny:
MORTAR: a rich client application for in silico molecule fragmentation. 1 - Henry Heberle, Linlin Zhao, Sebastian Schmidt, Thomas Wolf, Julian Heinrich:
XSMILES: interactive visualization for molecules, SMILES and XAI attribution scores. 2 - Olivier J. M. Béquignon, Brandon J. Bongers, Willem Jespers, Adriaan P. IJzerman, B. van der Water, Gerard J. P. van Westen:
Papyrus: a large-scale curated dataset aimed at bioactivity predictions. 3 - Shunsuke Tamura, Tomoyuki Miyao, Jürgen Bajorath:
Large-scale prediction of activity cliffs using machine and deep learning methods of increasing complexity. 4 - Dane R. Letourneau, Dennis D. August, Dietrich A. Volmer:
New algorithms demonstrate untargeted detection of chemically meaningful changing units and formula assignment for HRMS data of polymeric mixtures in the open-source constellation web application. 7 - Jonghwan Choi, Sangmin Seo, Sanghyun Park:
COMA: efficient structure-constrained molecular generation using contractive and margin losses. 8 - David H. Kenney, Randy C. Paffenroth, Michael T. Timko, Andrew R. Teixeira:
Dimensionally reduced machine learning model for predicting single component octanol-water partition coefficients. 9 - Salomé Rieder, Marina P. Oliveira, Sereina Riniker, Philippe H. Hünenberger:
Development of an open-source software for isomer enumeration. 10 - Vladimir Kondratyev, Marian Dryzhakov, Timur Gimadiev, Dmitriy Slutskiy:
Generative model based on junction tree variational autoencoder for HOMO value prediction and molecular optimization. 11 - Mingchen Li, Liqi Kang, Yi Xiong, Yu Guang Wang, Guisheng Fan, Pan Tan, Liang Hong:
SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering. 12 - Chu-I Yang, Yi-Pei Li:
Explainable uncertainty quantifications for deep learning-based molecular property prediction. 13 - Egon L. Willighagen:
Two years of explicit CiTO annotations. 14 - Rajarshi Guha, Barbara Zdrazil, Nina Jeliazkova, Karina Martínez-Mayorga:
A look back at a pilot of the citation typing ontology. 15 - Heval Atas Güvenilir, Tunca Dogan:
How to approach machine learning-based prediction of drug/compound-target interactions. 16 - Gao-Peng Ren, Yi-Jian Yin, Ke-Jun Wu, Yuchen He:
Force field-inspired molecular representation learning for property prediction. 17 - Manuel S. Sellner, Amr H. Mahmoud, Markus A. Lill:
Efficient virtual high-content screening using a distance-aware transformer model. 18 - Samuel Tovey, Fabian Zills, Francisco Torres-Herrador, Christoph Lohrmann, Marco Brückner, Christian Holm:
MDSuite: comprehensive post-processing tool for particle simulations. 19 - Paulo Neves, Kelly McClure, Jonas Verhoeven, Natalia Dyubankova, Ramil I. Nugmanov, Andrey Gedich, Sairam Menon, Zhicai Shi, Jörg K. Wegner:
Global reactivity models are impactful in industrial synthesis applications. 20 - Phyo Phyo Kyaw Zin:
Paths to cheminformatics: Q&A with Phyo Phyo Kyaw Zin. 21 - Linde Schoenmaker, Olivier J. M. Béquignon, Willem Jespers, Gerard J. P. van Westen:
UnCorrupt SMILES: a novel approach to de novo design. 22 - Felix Bänsch, Christoph Steinbeck, Achim Zielesny:
Notes on molecular fragmentation and parameter settings for a dissipative particle dynamics study of a C10E4/water mixture with lamellar bilayer formation. 23 - Xuhan Liu, Kai Ye, Herman W. T. van Vlijmen, Adriaan P. IJzerman, Gerard J. P. van Westen:
DrugEx v3: scaffold-constrained drug design with graph transformer-based reinforcement learning. 24 - Andrius Merkys, Antanas Vaitkus, Algirdas Grybauskas, Aleksandras Konovalovas, Miguel Quirós, Saulius Grazulis:
Graph isomorphism-based algorithm for cross-checking chemical and crystallographic descriptions. 25 - Umit V. Ucak, Islambek Ashyrmamatov, Juyong Lee:
Reconstruction of lossless molecular representations from fingerprints. 26 - Yuanbing Song, Jinghua Chen, Wenju Wang, Gang Chen, Zhichong Ma:
Double-head transformer neural network for molecular property prediction. 27 - Jim Boelrijk, Denice van Herwerden, Bernd Ensing, Patrick Forré, Saer Samanipour:
Predicting RP-LC retention indices of structurally unknown chemicals from mass spectrometry data. 28 - Chengyou Liu, Yan Sun, Rebecca L. Davis, Silvia T. Cardona, Pingzhao Hu:
ABT-MPNN: an atom-bond transformer-based message-passing neural network for molecular property prediction. 29 - Paulo Neves, Kelly McClure, Jonas Verhoeven, Natalia Dyubankova, Ramil I. Nugmanov, Andrey Gedich, Sairam Menon, Zhicai Shi, Jörg K. Wegner:
Correction: Global reactivity models are impactful in industrial synthesis applications. 30 - Ammar Ammar, Rachel Cavill, Chris T. A. Evelo, Egon L. Willighagen:
PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity. 31 - Mahnoor Zulfiqar, Luiz M. R. Gadelha Jr., Christoph Steinbeck, Maria Sorokina, Kristian Peters:
MAW: the reproducible Metabolome Annotation Workflow for untargeted tandem mass spectrometry. 32 - Mengdie Xu, Xinwei Zhao, Jingyu Wang, Wei Feng, Naifeng Wen, Chunyu Wang, Junjie Wang, Yun Liu, Lingling Zhao:
DFFNDDS: prediction of synergistic drug combinations with dual feature fusion networks. 33 - Aileen Bahl, Celine Ibrahim, Kristina Plate, Andrea Haase, Jörn Dengjel, Penny Nymark, Verónica I. Dumit:
PROTEOMAS: a workflow enabling harmonized proteomic meta-analysis and proteomic signature mapping. 34 - Chaofeng Lou, Hongbin Yang, Hua Deng, Mengting Huang, Weihua Li, Guixia Liu, Philip W. Lee, Yun Tang:
Chemical rules for optimization of chemical mutagenicity via matched molecular pairs analysis and machine learning methods. 35 - Jonathan Lam, Richard J. Lewis, Jonathan M. Goodman:
Interpreting vibrational circular dichroism spectra: the Cai•factor for absolute configuration with confidence. 36 - Roman Joeres, Daniel Bojar, Olga V. Kalinina:
GlyLES: Grammar-based Parsing of Glycans from IUPAC-condensed to SMILES. 37 - Yangyang Chen, Zixu Wang, Lei Wang, Jianmin Wang, Pengyong Li, Dongsheng Cao, Xiangxiang Zeng, Xiucai Ye, Tetsuya Sakurai:
Deep generative model for drug design from protein target sequence. 38 - Bryan Queme, John C. Braisted, Patricia Dranchak, James Inglese:
qHTSWaterfall: 3-dimensional visualization software for quantitative high-throughput screening (qHTS) data. 39 - Sascha Jung, Helge Vatheuer, Paul Czodrowski:
VSFlow: an open-source ligand-based virtual screening tool. 40 - Marta Pasquini, Marco Stenta:
LinChemIn: SynGraph - a data model and a toolkit to analyze and compare synthetic routes. 41 - Xiaohong Liu, Wei Zhang, Xiaochu Tong, Feisheng Zhong, Zhaojun Li, Zhaoping Xiong, Jiacheng Xiong, Xiaolong Wu, Zunyun Fu, Xiaoqin Tan, Zhiguo Liu, Sulin Zhang, Hualiang Jiang, Xutong Li, Mingyue Zheng:
MolFilterGAN: a progressively augmented generative adversarial network for triaging AI-designed molecules. 42 - Kexin Chen, Guangyong Chen, Junyou Li, Yuansheng Huang, Ercheng Wang, Tingjun Hou, Pheng-Ann Heng:
MetaRF: attention-based random forest for reaction yield prediction with a few trails. 43 - Kostas Blekos, Kostas Chairetakis, Iseult Lynch, Effie Marcoulaki:
Principles and requirements for nanomaterial representations to facilitate machine processing and cooperation with nanoinformatics tools. 44 - Shumpei Nemoto, Tadahaya Mizuno, Hiroyuki Kusuhara:
Investigation of chemical structure recognition by encoder-decoder models in learning progress. 45 - Eva Nittinger, Alex Clark, Anna Gaulton, Barbara Zdrazil:
Biomedical data analyses facilitated by open cheminformatics workflows. 46 - Markus Dablander, Thierry Hanser, Renaud Lambiotte, Garrett M. Morris:
Exploring QSAR models for activity-cliff prediction. 47 - Su-Qing Yang, Liu-Xia Zhang, You-Jin Ge, Jin-Wei Zhang, Jian-Xin Hu, Cheng-Ying Shen, Ai-Ping Lu, Tingjun Hou, Dong-Sheng Cao:
In-silico target prediction by ensemble chemogenomic model based on multi-scale information of chemical structures and protein sequences. 48 - Thomas-Martin Dutschmann, Lennart Kinzel, Antonius ter Laak, Knut Baumann:
Large-scale evaluation of k-fold cross-validation ensembles for uncertainty estimation. 49 - Nalini Schaduangrat, Nuttapat Anuwongcharoen, Phasit Charoenkwan, Watshara Shoombuatong:
DeepAR: a novel deep learning-based hybrid framework for the interpretable prediction of androgen receptor antagonists. 50 - Doris Schicker, Satnam Singh, Jessica Freiherr, Andreas Grasskamp:
OWSum: algorithmic odor prediction and insight into structure-odor relationships. 51 - Eleftheria Kontou, Axel Walter, Oliver Alka, Julianus Pfeuffer, Timo Sachsenberg, Omkar S. Mohite, Matin Nuhamunada, Oliver Kohlbacher, Tilmann Weber:
UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis. 52 - Katharina Dost, Zac Pullar-Strecker, Liam Brydon, Kunyang Zhang, Jasmin Hafner, Patricia J. Riddle, Jörg S. Wicker:
Combatting over-specialization bias in growing chemical databases. 53 - Rajarshi Guha, Darrell Velegol:
Harnessing Shannon entropy-based descriptors in machine learning models to enhance the prediction accuracy of molecular properties. 54 - Umit V. Ucak, Islambek Ashyrmamatov, Juyong Lee:
Improving the quality of chemical language model outcomes with atom-in-SMILES tokenization. 55 - Srijit Seal, Hongbin Yang, Maria-Anna Trapotsi, Satvik Singh, Jordi Carreras Puigvert, Ola Spjuth, Andreas Bender:
Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data. 56 - Zimei Zhang, Gang Wang, Rui Li, Lin Ni, Runze Zhang, Kaiyang Cheng, Qun Ren, Xiangtai Kong, Shengkun Ni, Xiaochu Tong, Li Luo, Dingyan Wang, Xiaojie Lu, Mingyue Zheng, Xutong Li:
Tora3D: an autoregressive torsion angle prediction model for molecular 3D conformation generation. 57 - Junren Li, Lei Fang, Jian-Guang Lou:
RetroRanker: leveraging reaction changes to improve retrosynthesis prediction through re-ranking. 58 - Andrew E. Blanchard, Debsindhu Bhowmik, Zachary R. Fox, John Gounley, Jens Glaser, Belinda S. Akpa, Stephan Irle:
Adaptive language model training for molecular design. 59 - Filippo Lunghini, Anna Fava, Vincenzo Pisapia, Francesco Sacco, Daniela Iaconis, Andrea Rosario Beccari:
ProfhEX: AI-based platform for small molecules liability profiling. 60 - Jakub Galgonek, Jirí Vondrásek:
A comparison of approaches to accessing existing biological and chemical relational databases via SPARQL. 61 - Charles Tapley Hoyt, Barbara Zdrazil, Rajarshi Guha, Nina Jeliazkova, Karina Martínez-Mayorga, Eva Nittinger:
Improving reproducibility and reusability in the Journal of Cheminformatics. 62 - Xujun Zhang, Chao Shen, Dejun Jiang, Jintu Zhang, Qing Ye, Lei Xu, Tingjun Hou, Peichen Pan, Yu Kang:
TB-IECS: an accurate machine learning-based scoring function for virtual screening. 63 - Michael A. Cunningham, Danielle Pins, Zoltán Dezso, Maricel Torrent, Aparna Vasanthakumar, Abhishek Pandey:
PINNED: identifying characteristics of druggable human proteins using an interpretable neural network. 64 - Jun-Xuan Jin, Gao-Peng Ren, Jianjian Hu, Yingzhe Liu, Yunhu Gao, Ke-Jun Wu, Yuchen He:
Force field-inspired transformer network assisted crystal density prediction for energetic materials. 65 - Parker Ladd Bremer, Gert Wohlgemuth, Oliver Fiehn:
The BinDiscover database: a biology-focused meta-analysis tool for 156,000 GC-TOF MS metabolome samples. 66 - Kenza Amara, Raquel Rodríguez-Pérez, José Jiménez-Luna:
Explaining compound activity predictions with a substructure-aware loss for graph neural networks. 67 - Umit V. Ucak, Islambek Ashyrmamatov, Juyong Lee:
Correction: Reconstruction of lossless molecular representations from fingerprints. 68 - Pawan Panwar, Quanpeng Yang, Ashlie Martini:
PyL3dMD: Python LAMMPS 3D molecular descriptors package. 69 - Umit V. Ucak, Islambek Ashyrmamatov, Juyong Lee:
Correction: Improving the quality of chemical language model outcomes with atom-in-SMILES tokenization. 70 - Hyun Woo Kim, Chen Zhang, Raphael Reher, Mingxun Wang, Kelsey L. Alexander, Louis-Félix Nothias, Yoo Kyong Han, Hyeji Shin, Ki Yong Lee, Kyu Hyeong Lee, Myeong Ji Kim, Pieter C. Dorrestein, William H. Gerwick, Garrison W. Cottrell:
DeepSAT: Learning Molecular Structures from Nuclear Magnetic Resonance Data. 71 - Baiqing Li, Shimin Su, Chan Zhu, Jie Lin, Xinyue Hu, Lebin Su, Zhunzhun Yu, Kuangbiao Liao, Hongming Chen:
A deep learning framework for accurate reaction prediction and its application on high-throughput experimentation data. 72 - Davide Boldini, Francesca Grisoni, Daniel Kuhn, Lukas Friedrich, Stephan A. Sieber:
Practical guidelines for the use of gradient boosting for molecular property prediction. 73 - Marina Gorostiola González, Remco L. van den Broek, Thomas G. M. Braun, Magdalini Chatzopoulou, Willem Jespers, Adriaan P. IJzerman, Laura H. Heitman, Gerard J. P. van Westen:
3DDPDs: describing protein dynamics for proteochemometric bioactivity prediction. A case for (mutant) G protein-coupled receptors. 74 - Yumeng Zhang, Janosch Menke, Jiazhen He, Eva Nittinger, Christian Tyrchan, Oliver Koch, Hongtao Zhao:
Similarity-based pairing improves efficiency of siamese neural networks for regression tasks and uncertainty quantification. 75 - Yitian Wang, Jiacheng Xiong, Fu Xiao, Wei Zhang, Kaiyang Cheng, Jingxin Rao, Buying Niu, Xiaochu Tong, Ning Qu, Runze Zhang, Dingyan Wang, Kaixian Chen, Xutong Li, Mingyue Zheng:
LogD7.4 prediction enhanced by transferring knowledge from chromatographic retention time, microscopic pKa and logP. 76 - Bongsung Bae, Haelee Bae, Hojung Nam:
LOGICS: Learning optimal generative distribution for designing de novo chemical structures. 77 - Rita Lasfar, Gergely Tóth:
Patch seriation to visualize data and model parameters. 78 - Rodrigo Ochoa, J. B. Brown, Thomas Fox:
pyPept: a python library to generate atomistic 2D and 3D representations of peptides. 79 - Gerard Baquer, Lluc Sementé, Pere Ràfols, Lucía Martín-Saiz, Christoph Bookmeyer, José A. Fernández, Xavier Correig, María García-Altares:
rMSIfragment: improving MALDI-MSI lipidomics through automated in-source fragment annotation. 80 - Agnieszka Wojtuch, Tomasz Danel, Sabina Podlewska, Lukasz Maziarka:
Extended study on atomic featurization in graph neural networks for molecular property prediction. 81 - Karla Gonzalez-Ponce, Carolina Horta Andrade, Fiona M. I. Hunter, Johannes Kirchmair, Karina Martínez-Mayorga, José L. Medina-Franco, Matthias Rarey, Alexander Tropsha, Alexandre Varnek, Barbara Zdrazil:
School of cheminformatics in Latin America. 82 - Maud Parrot, Hamza Tajmouati, Vinicius Barros Ribeiro da Silva, Brian Ross Atwood, Robin Fourcade, Yann Gaston-Mathé, Nicolas Do Huu, Quentin Perron:
Integrating synthetic accessibility with AI-based generative drug design. 83 - Thomas E. Hadfield, Jack Scantlebury, Charlotte M. Deane:
Exploring the ability of machine learning-based virtual screening models to identify the functional groups responsible for binding. 84 - Christopher Secker, Konstantin Fackeldey, Marcus Weber, Sourav Ray, Christoph Gorgulla, Christof Schütte:
Novel multi-objective affinity approach allows to identify pH-specific μ-opioid receptor agonists. 85 - Prasannavenkatesh Durai, Sue Jung Lee, Jae Wook Lee, Cheol-Ho Pan, Keunwan Park:
Iterative machine learning-based chemical similarity search to identify novel chemical inhibitors. 86 - Ximin Hu, Derek Mar, Nozomi Suzuki, Bowei Zhang, Katherine T. Peter, David A. C. Beck, Edward P. Kolodziej:
Mass-Suite: a novel open-source python package for high-resolution mass spectrometry data analysis. 87 - Lai Wei, Nihang Fu, Yuqi Song, Qian Wang, Jianjun Hu:
Probabilistic generative transformer language models for generative design of molecules. 88 - Alan Kerstjens, Hans De Winter:
A molecule perturbation software library and its application to study the effects of molecular design constraints. 89 - Jackson J. Alcázar, Alessandra C. Misad Saide, Paola R. Campodónico:
Reliable and accurate prediction of basic pKa values in nitrogen compounds: the pKa shift in supramolecular systems as a case study. 90 - Chao Hu, Song Li, Chenxing Yang, Jun Chen, Yi Xiong, Guisheng Fan, Hao Liu, Liang Hong:
ScaffoldGVAE: scaffold generation and hopping of drug molecules via a variational autoencoder based on multi-view graph neural networks. 91 - Nathaniel Charest:
Paths to cheminformatics: Q&A with Nathaniel Charest. 92 - Ann M. Richard:
Paths to cheminformatics: Q&A with Ann M. Richard. 93 - Mehrdad Jalali, A. D. Dinga Wonanke, Christof Wöll:
MOFGalaxyNet: a social network analysis for predicting guest accessibility in metal-organic frameworks utilizing graph convolutional networks. 94 - Jorge Medina, Andrew D. White:
Bloom filters for molecules. 95 - Alberto Gil Pichardo, Andrés Sánchez-Ruiz, Gonzalo Colmenarejo:
Analysis of metabolites in human gut: illuminating the design of gut-targeted drugs. 96 - Nan Song, Ruihan Dong, Yuqian Pu, Ercheng Wang, Junhai Xu, Fei Guo:
Pmf-cpi: assessing drug selectivity with a pretrained multi-functional model for compound-protein interactions. 97 - Venkata Chandrasekhar, Nisha Sharma, Jonas Schaub, Christoph Steinbeck, Kohulan Rajan:
Cheminformatics Microservice: unifying access to open cheminformatics toolkits. 98 - Arash Tayyebi, Ali S. Alshami, Zeinab Rabiei, Xue Yu, Nadhem Ismail, Musabbir Jahan Talukder, Jason Power:
Prediction of organic compound aqueous solubility using machine learning: a comparison study of descriptor-based and fingerprints-based models. 99 - Daniela Gaytán-Hernández, Ana L. Chávez-Hernández, Edgar López-López, Jazmín Miranda-Salas, Fernanda I. Saldívar-González, José L. Medina-Franco:
Art driven by visual representations of chemical space. 100 - Zachary Fralish, Ashley Chen, Paul Skaluba, Daniel Reker:
DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning. 101 - Guzel Minibaeva, Aleksandra Ivanova, Pavel G. Polishchuk:
EasyDock: customizable and scalable docking tool. 102 - Shihang Wang, Lin Wang, Fenglei Li, Fang Bai:
DeepSA: a deep-learning driven predictor of compound synthesis accessibility. 103 - Sean Li, Björn Bohman, Gavin R. Flematti, Dylan Jayatilaka:
Determining the parent and associated fragment formulae in mass spectrometry via the parent subformula graph. 104 - Tianzhixi Yin, Gihan Panapitiya, Elizabeth D. Coda, Emily Saldanha:
Evaluating uncertainty-based active learning for accelerating the generalization of molecular property prediction. 105 - Gil Alon, Yuval Ben-Haim, Inbal Tuvi-Arad:
Continuous symmetry and chirality measures: approximate algorithms for large molecular structures. 106 - Daniel Domingo-Fernández, Yojana Gadiya, Sarah Mubeen, David Healey, Bryan H. Norman, Viswa Teja S. S. Colluru:
Exploring the known chemical space of the plant kingdom: insights into taxonomic patterns, knowledge gaps, and bioactive regions. 107 - Mehrdad Jalali, A. D. Dinga Wonanke, Christof Wöll:
Correction: MOFGalaxyNet: a social network analysis for predicting guest accessibility in metal-organic frameworks utilizing graph convolutional networks. 108 - Chong Zhou, Wei Liu, Xiyue Song, Mengling Yang, Xiaowang Peng:
YoDe-Segmentation: automated noise-free retrieval of molecular structures from scientific publications. 111 - Koichi Handa, Morgan C. Thomas, Michiharu Kageyama, Takeshi Iijima, Andreas Bender:
On the difficulty of validating molecular generative models realistically: a case study on public and proprietary data. 112 - Daniel Probst:
An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification. 113 - Herman Rull, Markus Fischer, Stefan Kuhn:
NMR shift prediction from small data quantities. 114 - Hong-Quan Xu, Huan Xiao, Jin-Hui Bu, Yan-Feng Hong, Yu-Hong Liu, Zi-Yue Tao, Shu-Fan Ding, Yi-Tong Xia, E. Wu, Zhen Yan, Wei Zhang, Gong-Xing Chen, Feng Zhu, Lin Tao:
EMNPD: a comprehensive endophytic microorganism natural products database for prompt the discovery of new bioactive substances. 115 - Etienne Lehembre, Johanna Giovannini, Damien Geslin, Alban Lepailleur, Jean Luc Lamotte, David Auber, Abdelkader Ouali,