Available PhD position for Molecular Modeling and Virtual Screening for Rational Design of Tubulin-Protein Interaction Modulators at TubInTrain (European Joint Doctorate on chemistry and biology). For further info: https://www.tubintrain.eu/phd-project-esr2/
The valedictory lecture of Prof. Todeschini will take place on monday 16th of december 2019, 10:30, room U3-01 (building U3 – Università degli Studi di Milano – Bicocca): “Una vita per la ricerca, la ricerca di una vita” [link]
In this new release (5.4), class modelling methods (SIMCA, Potential Functions – Kernel Density Estimators, UNEQ) can now be calculated on a specific target class. The toolbox can be downloaded here.
The 23rd EuroQSAR Symposium, entitled “Integrative Data-Intensive Approaches to Drug Design” will take place in Barcelona, September 20-24, 2020: https://www.euroqsar2020.org/
In this new release, batch training has been optimised and now it is faster than previous versions (computational time has been decreased on about 60%). RMSE (squared residuals between sampels and weights of winning neurons) and the average changing of the topological distance between previous and updated winning neurons can be plotted as a function of epochs during training.
The toolbox can be downloaded here.
Durante l’Assemblea dei Soci della Divisione di Chimica Analiticadella Società Chimica Italiana è stata conferita al Prof. Roberto Todeschini la Medaglia Canneri in riconoscimento del suo contributo allo sviluppo e alla divulgazione della chemiometria.
New publication: Deep Ranking Analysis by Power Eigenvectors (DRAPE): A wizard for ranking and multi-criteria decision making, have a look here!
We had a collaboration with Univerisity of Azuay since the late ’80. Today, we participated to the cerimony for the official signature of the Memorandum of Understanding between our University and the Univerisity of Azuay (Cuenca, Equador), represented by its Rector, Prof. Francisco Salgado Arteaga.
Have a look to our latest publication: Grisoni, F., Consonni, V., Ballabio, D. (2019) Machine Learning Consensus to Predict the Binding to the Androgen Receptor within the CoMPARA project. Journal of chemical information and modeling, 59, 1839-1848 [link]
Data related to these models are available for download.