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Record 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 on OpenAlexaboutno aff
Mattia Mori, Fabrizio Manetti, Bruno Botta, Andrea Tafi

Bibliographic record

VenueJournal of Chemical Information and Modeling · 2019
Typeeditorial
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsnot available
Fundersnot available
KeywordsComputer-aidedDrugComputer sciencePsychologyPsychiatry

Abstract

fetched live from 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.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.085
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.024
GPT teacher head0.296
Teacher spread0.272 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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".

Quick stats

Citations2
Published2019
Admission routes1
Has abstractyes

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