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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".