MétaCan
Menu
Retour à la cohorte
Enregistrement W2992379148 · doi:10.1021/acs.jcim.9b01043

In Memory of Maurizio Botta: His Contribution to the Development of Computer-Aided Drug Design

2019· editorial· en· W2992379148 sur OpenAlexaboutno aff
Mattia Mori, Fabrizio Manetti, Bruno Botta, Andrea Tafi

Notice bibliographique

RevueJournal of Chemical Information and Modeling · 2019
Typeeditorial
Langueen
DomaineComputer Science
ThématiqueComputational Drug Discovery Methods
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésComputer-aidedDrugComputer sciencePsychologyPsychiatry

Résumé

récupéré en direct d'OpenAlex

Maurizio Botta was born in Manziana, near Rome (Italy), on May 1950. He built his background in chemistry at Sapienza University of Rome, and then, he received his Ph.D. in 1979 at the University of Brunswick (Canada) working at the total synthesis of complex natural products under the supervision of Prof. Karel Wiesner. From 1980 to 1987, he was a Researcher at the Sapienza University of Roma, and thanks to a NATO grant, he spent one year in 1985–1986 working as a postdoctoral associate in the group of Prof. Stephen Hanessian at the University of Montreal (Canada), where he was also invited as a visiting scientist many other times thereafter.(1) In 1987, he became an Associate Professor of medicinal chemistry at the University of Siena (Italy) and then a Full Professor in 2000. His scientific career was objectively successful, as he was author in more than 400 papers and books or book chapters, as well as inventor in more than 25 patents. He was a member of many scientific societies and editorial boards of journals mostly devoted to chemistry, medicinal chemistry and drug design. Particularly, he served as an Associate Editor for ACS Medicinal Chemistry Letters. Besides his chemical background, in his research life Maurizio was intrigued by multiple fields adjacent to synthetic chemistry, such as biochemistry, biophysics, molecular biology, and computational modeling. This latter captured his attention and was implemented in his research activity as a crucial support to drug design since the beginning of the 1990s. It is worth noting that most of Botta’s publications report on the use of computational tools, mostly relying on (but not limited to) molecular mechanics (MM) approaches to rationalize existing biological data, or to drive the design and synthesis of bioactive compounds. His research initially focused on conformational analysis of small molecules with the double aim to seize the enormous possibilities offered by MM in modeling conformational flexibility detected by NMR spectroscopy, as well as to exploit the active analogue approach (AAA) developed by Prof. Garland Marshall to interpreting pharmacological properties of bioactive compounds.(2) Thanks to the fruitful collaboration with Prof. Kosta Steliou (University of Montreal), near the beginning of the 1990s, his group started to use the VAX version of Model software, implemented with the MMX force field that was especially suitable to modeling molecular systems with π-electron delocalization. In the same years, MacroModel was used to model with high accuracy the flexibility of an increased number of organic molecules thanks to different force fields, while the use of Sybyl was initially related to its graphic potential but would later pave the way to using the CoMFA 3D-QSAR methodology. Since then, the role of computational modeling studies became more and more relevant in the research of Maurizio’s group, up to the publication of papers mostly or exclusively conducted at the theoretical level. The crucial role of theoretical approaches in drug design was highlighted by the successful series of workshops, i.e., the European Workshop in Drug Design (EWDD), held every two years in the lovely location of Certosa di Pontignano in the countryside of Siena (Italy). The aim of this paper is to honor the memory of Prof. Maurizio Botta and to briefly overview the major contributions he gave to the field of computer-aided drug design, chemical information, and modeling. Works that represented a milestone in his research strategy are briefly overviewed herein, grouped on the basis of the topic. Finally, a note to the EWDD series is provided.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Simulation ou modélisation · Signal consensuel: Simulation ou modélisation
GenreSignal candidat: Méthodes · Signal consensuel: Méthodes
Score de désaccord entre enseignants0,085
Score d'incertitude au seuil0,457

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,024
Tête enseignante GPT0,296
Écart entre enseignants0,272 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSimulation ou modélisation
Domainenon disponible
GenreMéthodes

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2019
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJournal of Chemical Information and ModelingMême sujetComputational Drug Discovery MethodsTravaux en français237 207