Riccardo Zanni

QSAR multi-target in drug discovery: a review.

The main purpose of the present review is to summarize the most significant works up to date in the field of multi-target QSAR (mt-QSAR), in order to emphasize the importance that this technique has acquired over the last decade. Unlike traditional QSAR techniques, mt-QSAR permits to calculate the probability of activity of a given compound against different biological or pharmacological targets. In simple terms, a single equation for multiple outputs. To emphasize more the importance of the mt-QSAR in the field of drug discovery, we also present a novel mt-QSAR model, made on purpose by our research group, for the prediction of the susceptibility of Gram + and Gram - anaerobic bacteria.

Latest advances in molecular topology applications for drug discovery

Introduction: Molecular topology (MT) has emerged in recent years as a powerful approach for the in silico generation of new drugs. In the last decade, its application has become more and more popular among the leading research groups in the field of quantitative structure–activity relationships (QSAR) and drug design. This has, in turn, contributed to the rapid development of new techniques and applications of MT in QSAR studies, as well as the introduction of new topological indices. Areas covered: This review collates the main innovative techniques in the field of MT and provides a description of the novel topological indices recently introduced, through an exhaustive recompilation of the most significant works carried out by the leading research groups in the field of drug design and discovery. The objective is to show the importance of MT methods combined with the effectiveness of the descriptors. Expert opinion: Recent years have witnessed a remarkable rise in QSAR methods based on MT and its application to drug design. New methodologies have been introduced in the area such as QSAR multi-target, Markov networks or perturbation methods. Moreover, novel topological indices, such as Bourgas’ descriptors and other new concepts as the derivative of a graph or cliques capable to distinguish between conformers, have also been introduced. New drugs have also been discovered, including anticonvulsants, anineoplastics, antimalarials or antiallergics, just to name a few. In the authors’ opinion, MT and QSAR have moved from an attractive possibility to representing a foundation stone in the process of drug discovery.

Predicting antiprotozoal activity of benzyl phenyl ether diamine derivatives through QSAR multi-target and molecular topology

Multi-target QSAR is a novel approach that can predict simultaneously the activity of a given chemical compound on different pharmacological targets. In this work, we have used molecular topology and statistical tools such as multilinear regression analysis and artificial neural networks, to achieve a multi-target QSAR model capable to predict the antiprotozoal activity of a group of benzyl phenyl ether diamine derivatives. The activity was related to three parasites with a high prevalence rate in humans: Trypanosoma brucei rhodesiense, Plasmodium falciparum, and Leishmania donovani. The multi-target model showed a high regression coefficient (

Novel Cancer Chemotherapy Hits by Molecular Topology: Dual Akt and Beta-Catenin Inhibitors

{R}^{2} = 0.9644

Modeling Anti-Allergic Natural Compounds by Molecular Topology

R2=0.9644 and

Advances in the molecular modeling and quantitative structure–activity relationship-based design for antihistamines

{R}^{2} = 0.9235