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Alberto Manganaro

Researcher at Mario Negri Institute for Pharmacological Research

Publications -  45
Citations -  1968

Alberto Manganaro is an academic researcher from Mario Negri Institute for Pharmacological Research. The author has contributed to research in topics: Applicability domain & Computer science. The author has an hindex of 18, co-authored 39 publications receiving 1434 citations. Previous affiliations of Alberto Manganaro include University of Milano-Bicocca.

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CAESAR models for developmental toxicity

TL;DR: Two QSAR models for developmental toxicity have been developed, using different statistical/mathematical methods, with the aim to minimize false negatives in order to make them more usable for REACH.
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Automatic knowledge extraction from chemical structures: the case of mutagenicity prediction.

TL;DR: This work proposes a new structure–activity relationship (SAR) approach to mine molecular fragments that act as structural alerts for biological activity, and has been tested on the mutagenicity endpoint, showing marked prediction skills and bringing to the surface much of the knowledge already collected in the literature as well as new evidence.

VEGA-QSAR: AI Inside a Platform for Predictive Toxicology.

TL;DR: An initiative aimed to establish a dialogue within the community of scientists, regulators, industry representatives, offering a platform which combines the predictive capability of computer models, with some explanation tools, which may be convincing and helpful for human users to derive a conclusion.
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Comparison of in silico tools for evaluating rat oral acute toxicity.

TL;DR: Five software programs for the evaluation of mammalian acute toxicity, exploring acute oral toxicity data expressed as median lethal dose (LD50), and found that all models gave high performance for certain classes while other classes were always badly predicted.
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A generalizable definition of chemical similarity for read-across

TL;DR: The comparison of multiple combinations of binary fingerprints and similarity metrics for computing the chemical similarity in the context of two different applications of the read-across technique demonstrates that the classical similarity measurements can be improved with a generalizable model of similarity.