03/07/2024
Happy to announce that our project MolBook Pro was awarded at the Contamination Lab, the laboratory of the University of Pisa which trains students, doctoral and post-doc students on the topics of entrepreneurship, and innovation, providing the tools for the development of entrepreneurial ideas, the creation of business models and the creation of academic startups.
Archivio dell'autore: Salvatore Galati
Beta Release 1.4 of MolBook UNIPI is now available!
11/10/2023 – Beta Release 1.4 of MolBook UNIPI is available. Try it!
Click here to download it.
Release 1.4 Beta:
- Added model for prediction of binding to plasma albumin in the ADMEPred tool
- Added ReactionSearcher tool
- Included the possibility of protonating molecules at physiological pH
- Added minimization process during 3D coordinate generation
- Fixed bug when exporting sdf files (occured if the database had field whose name started with “_”)
John Laird Telford award for our PhD Student Miriana Di Stefano
27/09/2023
Our PhD student Miriana Di Stefano received the John Laird Telford Award, conceived by Prof. Cosima Tatiana Baldari, for the Best Oral Presentation of her doctoral project at the Department of Life Sciences, University of Siena.
MolBook UNIPI was presented in Thessaloniki by our PhD student Salvatore Galati
18/07/2023
MolBook UNIPI was presented at the XII Paul Ehrlich PhD NetWork in Thessaloniki by our PhD student Salvatore Galati
Congratulations to our PhD student Miriana Di Stefano selected for oral presentation at ESMEC 2023
Congratulations to our PhD student Miriana Di Stefano for her presentation at RDKit UGM2022
12/10/2022 – Miriana Di Stefano, a doctoral student of the mmvsl group, has taken part in 11th RDKit UGM2022 Conference in Berlin presenting VenomPred Platform with an oral presentation and a poster.
For more details: https://github.com/rdkit/UGM_2022
Congratulations to our doctoral student Salvatore Galati for his presentation at MMCS 2022
08/09/2022 – Salvatore Galati, a PhD student gave an oral comunication entitled “VenomPred: A Machine Learning Based Platform for Molecular Toxicity Predictions” at the MMCS 2022 meeting (https://mmcs2022.sciforum.net/).