Publications — Antonio de la Vega de León

Peer-reviewed journal articles

Torrisi M, Asadollahi S, de la Vega de León A, Wang K, Copeland W. Do chemical language models provide a better compound representation? bioRxiv 2023.11.07.566025. Link
Asad A, Shahidan NO, de la Vega de León A, Wiggin GR, Whitfield TT, Baxendale S. A screen of pharmacologically active compounds to identify modulators of the Adgrg6/Gpr126 signalling pathway in zebrafish embryos. Basic Clin Pharmacol Toxicol. 2023; 133(4): 364–377. Link
Walter M, Allen LN, de la Vega de León A, Webb SJ & Gillet VJ. Analysis of the benefits of imputation models over traditional QSAR models for toxicity prediction. J Cheminform 14, 32, 2022. Link
Torrisi M, de la Vega de León A, Climent G, Loos R & Panjkovich A. Improving the Assessment of Deep Learning Models in the Context of Drug-Target Interaction Prediction. bioRxiv 2022.04.20.488898. Link
Kenyon EJ, Kirkwood NK, Kitcher SR, Goodyear RJ, Derudas M, Cantillon DM, Baxendale S, de la Vega de León A, Mahieu VN, Osgood RT, Wilson CD, Bull JC, Waddell SJ, Whitfield TT, Ward SE, Kros CJ & Richardson GP. Identification of a series of hair-cell MET channel blockers that protect against aminoglycoside-induced ototoxicity. JCI Insight. 2021;6(7):e145704. Link
Grayson JD, Baumgartner MP, Dos Santos Souza C, Dawes SJ, Ghafir El Idrissi I, Louth JC, Stimpson S, Mead E, Dunbar C, Wolak J, Sharman G, Evans D, Zhuravleva A, Segovia Roldan M, Colabufo NA, Ning K, Garwood C, Thomas JA, Partridge BM, de la Vega de León A, Gillet VJ, Rauter AP & Chen B. Amyloid binding and beyond: a new approach for Alzheimer's disease drug discovery targeting Aβo–PrPC binding and downstream pathways. Chem. Sci., 2021. Link
Bates J, Cameron D, Checco A, Clough P, Hopfgartner F, Mazumdar S, Sbaffi L, Stordy P & de la Vega de León A. Integrating FATE/critical data studies into data science curricula: where are we going and how do we get there? Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT* '20). ACM, 425–435, 2020. Link
Diamantopolou E, Baxendale S, de la Vega de León A, Asad A, Holdsworth CJ, Abbas L, Gillet VJ, Wiggin GR & Whitfield TT. Identification of compounds that rescue otic and myelination defects in the zebrafish adgrg6 (gpr126) mutant. eLife 8:44889, 2019. Link
de la Vega de León A, Chen B & Gillet VJ. Effect of missing data on multitask prediction methods. J Cheminf 10:26, 2018. Link
de la Vega de León A & Bajorath J. Design of chemical space networks incorporating compound distance relationships. F1000Research 5(Chem Inf Sci):2634, 2016. Link
Anighoro A, de la Vega de León A & Bajorath J. Predicting bioactive conformations and binding modes of macrocycles. J Comput-Aided Mol Des 30, 841, 2016. Link
de la Vega de León A & Bajorath J. Chemical space visualization: transforming multi-dimensional chemical spaces into similarity-based molecular networks. Future Med Chem 8, 1769–1778, 2016. Link
Horvath D, Marcou G, Varnek A, Kayastha S, de la Vega de León A & Bajorath J. Prediction of activity cliffs using condensed graphs of reaction representations, descriptor recombination, support vector machine classification, and support vector regression. J Chem Inf Model 56, 1631–1640, 2016. Link
Shanmugasundaram V, Zhang L, Kayastha S, de la Vega de León A, Dimova D & Bajorath J. Monitoring the progression of structure-activity relationship information during lead optimization. J Med Chem 59, 4235–4244, 2016. Link
de la Vega de León A, Kayastha S, Dimova D, Schultz T & Bajorath J. Visualization of multi-property landscapes for compound selection and optimization. J Comput-Aided Mol Des 29, 695–705, 2015. Link
Hameed A, Khan K, Zehra S, Ahmed R, Shafiq Z, Bakht S, Yaqub M, Hussain M, de la Vega de León A, Furtmann N, Bajorath J, Ahmad H, Tahir M & Iqbal J. Synthesis, biological evaluation and molecular docking of N-phenyl thiosemicarbazones as urease inhibitors. Bioorg Chem 61, 51–57, 2015. Link
Kayastha S, de la Vega de León A, Dimova D & Bajorath J. Target-based analysis of ionization states of bioactive compounds. Med Chem Commun 6, 1030–1035, 2015. Link
de la Vega de León A & Bajorath J. Prediction of compound potency changes in matched molecular pairs using support vector regression. J Chem Inf Model 54, 2654–2663, 2014. Link
de la Vega de León A, Hu Y & Bajorath J. Systematic identification of matching molecular series and mapping of screening hits. Mol Inf 33, 257–263, 2014. Link
Stumpfe D, de la Vega de León A, Dimova D & Bajorath J. Advancing the activity cliff concept, part II. F1000Research 3:75, 2014. Link
de la Vega de León A & Bajorath J. Formation of activity cliffs is accompanied by systematic increases in ligand efficiency from lowly to highly potent compounds. AAPS J 16, 335–341, 2014. Link
Hu Y, de la Vega de León A, Zhang B & Bajorath J. Matched molecular pair-based data sets for computer-aided medicinal chemistry. F1000Research 3:36, 2014. Link
de la Vega de León A & Bajorath J. Matched molecular pairs derived by retrosynthetic fragmentation. Med Chem Commun 5, 64–67, 2014. Link
Fernandez de las Heras L, Alonso S, de la Vega de León A, Xavier D, Perera J & Navarro Llorens JM. Draft genome sequence of the steroid degrader Rhodococcus ruber Strain Chol-4. Genome Announc 1:e00215-13, 2013. Link
de la Vega de León A & Bajorath J. Compound optimization through data set-dependent chemical transformations. J Chem Inf Model 53, 1263–1271, 2013. Link
de la Vega de León A & Bajorath J. Design of a three-dimensional multi-target activity landscape. J Chem Inf Model 52, 2876–2883, 2012. Link

Software repositories

Effect of missing data on multitask prediction performance. GitHub

Book chapters

de la Vega de León A, Lounkine E, Vogt M & Bajorath J. Chapter 5: Design of diverse and focused compound libraries. In: Tutorials in Cheminformatics. Ed: Varnek A. Link
Vogt M, de la Vega de León A & Bajorath J. Chapter 24: Algorithmic chemoinformatics. In: Tutorials in Cheminformatics. Ed: Varnek A. Link

Oral presentations

Drug representation and scrambling experiments highlight issues with training and evaluation of drug-target interaction predictive models. Fall 2022 ACS National Meeting. Slides
Effect of missing data on multitask prediction models. OpenTox 2020 Virtual Conference. Slides
Effect of missing data on multitask prediction models. Invited speaker, Joint RSC-NSFC Symposium on Artificial Intelligence. Slides
Visualizations of chemical space. Invited speaker, Spanish Medicines Agency. Slides
Data visualization of the results of a large chemical screen. XXVI CNB Workshop Advances in Molecular Biology by Young Researchers Abroad. Slides
Phenotypic screening aided by multitask prediction methods. Fall 2018 Boston ACS National Meeting. Slides
Visualizations for chemical data. DataViz Hub launch event, University of Sheffield. Slides
Deep learning application to aid phenotypic assay campaigns with public chemical data. Fall 2017 Washington ACS National Meeting. Slides

Posters

de la Vega de León A, Torrisi M & Panjkovich A. Drug Representation and Scrambling Experiments Highlight Issues with Training and Evaluation of Drug-target Interaction Predictive Models. Ninth Joint Sheffield Conference on Chemoinformatics, 2023.
de la Vega de León A & Gillet VJ. Deep neural networks to support phenotypic screening campaigns. EuroQSAR 2018.
Baxendale S, Diamantopoulou E, Asad A, de la Vega de León A, Gillet VJ & Whitfield TT. Chemobiological analysis of the Adgrg6 signalling pathway in the zebrafish. 13th International Zebrafish Conference, 2018.
de la Vega de León A & Gillet VJ. Effect of missing data on multitask prediction performance. 11th International Conference on Chemical Structures, 2018. PDF
de la Vega de León A & Gillet VJ. Multitask machine learning for sparse chemical data sets. Insigneo Showcase Event, 2018. PDF
de la Vega de León A & Gillet VJ. Comparison of multitask prediction methods for chemical data. UK-QSAR and MGMS joint meeting, April 2018. PDF
de la Vega de León A & Gillet VJ. Deep learning application to aid phenotypic assay campaigns with public chemical data. 13th German Conference of Chemoinformatics (GCC 2017). PDF
Müller G, Benningshof J, van Meurs P, Stumpfe D, de la Vega de León A, Furtmann N, Dimova D & Bajorath J. Synthetic and cheminformatic exploration of macrocyclic and peptidomimetic medicinal chemistry space. EFMC-ISMC 2014.
Zhang L, Starr J, Dimova D, Iyer P, Gupta-Ostermann D, de la Vega de León A, Bajorath J, Shanmugasundaram V. Novel applications of SAR matrices in pharmaceutical research. Spring 2014 Dallas ACS National Meeting.
de la Vega de León A & Bajorath J. Compound optimization through data set-dependent chemical transformations. 9th German Conference on Chemoinformatics (GCC 2013). PDF

Data sets

Shanmugasundaram V, Liying Z, Kayastha S, de la Vega de León A, Dimova D & Bajorath J. Data sets for SAR progression analysis. Link
de la Vega de León A, Kayastha S, Dimova D, Schultz T & Bajorath J. ChEMBL20 data sets for multi-property landscape analysis. Link
Hu Y, de la Vega de León A, Zhang B & Bajorath J. Detailed data sets of MMP-cliffs, SAR transfer series, RECAP-MMPs and compound activities. Link

Blog posts

2025-10-09: Fingerprint comparison for ML models. Link