Bibliographic record
Abstract
Rafael Gozalbes holds a Ph.D. in Physical Chemistry from the University of Valencia, Spain (1998). After a post-doctoral position at the “Groupe de Chimie Informatique et Modelisation” (ITODYS) and Faculte de Medecine, both at the Universite Paris VII, he spent seven years as senior scientist at the Modelling group of CEREP, a biotech company, in France. Since 2007 he is scientific collaborator at the Structural Biochemistry Laboratory of the Centro de Investigacion Principe Felipe (CIPF). His current laboratory is actively engaged in the structural exploration of protein targets relevant in cell invasion and metastasis by using NMR spectroscopy, as well as fragment-based approaches for hit-identification. In this context, Dr. Gozalbes has the responsibility of the chemoinformatics and molecular modelling activities related to the group projects, and he has participated in several programs financed by public institutions as well as translational projects supported by pharmaceutical companies. Dr. Gozalbes is a computational chemist with expertise in the application of in silico approaches to drug discovery, and in particular the development of QSAR multivariate models (for physico-chemical, ADME-T and biological predictions), docking and study of protein-ligand interactions, design of virtual target-focused and diverse chemical libraries, pharmacophore hypotheses generation and virtual screening for selection of hit candidates. He has reviewed research programs for international institutions such as the Institut Pasteur – Cenci Bolognetti Foundation of Rome University “La Sapienza” (Italy) or the Fonds de recherche sur la nature et les technologies (FQRNT), Quebec, Canada. He has published more than 20 scientific papers and two book chapters, and collaborates regularly as a scientific reviewer of several computational and medicinal chemistry journals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.338 | 0.207 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".