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.Journal of cheminformatics. 2023, 15 (1): 100
Zachary Fralish, Ashley Chen, Paul Skaluba, Daniel Reker. DeepDelta: predicting ADMET improvements of molecular derivatives with deep learning.Journal of cheminformatics. 2023, 15 (1): 101
Shihang Wang, Lin Wang, Fenglei Li, Fang Bai. DeepSA: a deep-learning driven predictor of compound synthesis accessibility.Journal of cheminformatics. 2023, 15 (1): 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.Journal of cheminformatics. 2023, 15 (1): 104
Tianzhixi Yin, Gihan Panapitiya, Elizabeth D Coda, Emily G Saldanha. Evaluating uncertainty-based active learning for accelerating the generalization of molecular property prediction.Journal of cheminformatics. 2023, 15 (1): 105
Gil Alon, Yuval Ben-Haim, Inbal Tuvi-Arad. Continuous symmetry and chirality measures: approximate algorithms for large molecular structures.Journal of cheminformatics. 2023, 15 (1): 106
Daniel Domingo-Fernández, Yojana Gadiya, Sarah Mubeen, David Healey, Bryan H Norman, Viswa Colluru. Exploring the known chemical space of the plant kingdom: insights into taxonomic patterns, knowledge gaps, and bioactive regions.Journal of cheminformatics. 2023, 15 (1): 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.Journal of cheminformatics. 2023, 15 (1): 108
Ansar Naseem, Fahad Alturise, Tamim Alkhalifah, Yaser Daanial Khan. BBB-PEP-prediction: improved computational model for identification of blood-brain barrier peptides using blending position relative composition specific features and ensemble modeling.Journal of cheminformatics. 2023, 15 (1): 110
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.Journal of cheminformatics. 2023, 15 (1): 112
Daniel Probst. An explainability framework for deep learning on chemical reactions exemplified by enzyme-catalysed reaction classification.Journal of cheminformatics. 2023, 15 (1): 113