Riccardo Zanni

Bio: Riccardo Zanni is an academic researcher from University of Valencia. The author has contributed to research in topics: Quantitative structure–activity relationship & In silico. The author has an hindex of 7, co-authored 26 publications receiving 166 citations. Previous affiliations of Riccardo Zanni include University of Málaga & University of Bologna.

Modeling Natural Anti-Inflammatory Compounds by Molecular Topology

Abstract: One of the main pharmacological problems today in the treatment of chronic inflammation diseases consists of the fact that anti-inflammatory drugs usually exhibit side effects. The natural products offer a great hope in the identification of bioactive lead compounds and their development into drugs for treating inflammatory diseases. Computer-aided drug design has proved to be a very useful tool for discovering new drugs and, specifically, Molecular Topology has become a good technique for such a goal. A topological-mathematical model, obtained by linear discriminant analysis, has been developed for the search of new anti-inflammatory natural compounds. An external validation obtained with the remaining compounds (those not used in building up the model), has been carried out. Finally, a virtual screening on natural products was performed and 74 compounds showed actual anti-inflammatory activity. From them, 54 had been previously described as anti-inflammatory in the literature. This can be seen as a plus in the model validation and as a reinforcement of the role of Molecular Topology as an efficient tool for the discovery of new anti-inflammatory natural compounds.

Latest advances in molecular topology applications for drug discovery

Abstract: 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 applicat...

QSAR multi-target in drug discovery: a review.

Abstract: 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.

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

Abstract: Background and Purpose Colorectal and prostate cancers are two of the most common types and cause of a high rate of deaths worldwide. Therefore, any strategy to stop or at least slacken the development and progression of malignant cells is an important therapeutic choice. The aim of the present work is the identification of novel cancer chemotherapy agents. Nowadays, many different drug discovery approaches are available, but this paper focuses on Molecular Topology, which has already demonstrated its extraordinary efficacy in this field, particularly in the identification of new hit and lead compounds against cancer. This methodology uses the graph theoretical formalism to numerically characterize molecular structures through the so called topological indices. Once obtained a specific framework, it allows the construction of complex mathematical models that can be used to predict physical, chemical or biological properties of compounds. In addition, Molecular Topology is highly efficient in selecting and designing new hit and lead drugs. According to the aforementioned, Molecular Topology has been applied here for the construction of specific Akt/mTOR and β-catenin inhibition mathematical models in order to identify and select novel antitumor agents. Experimental Approach Based on the results obtained by the selected mathematical models, six novel potential inhibitors of the Akt/mTOR and β-catenin pathways were identified. These compounds were then tested in vitro to confirm their biological activity. Conclusion and Implications Five of the selected compounds, CAS n° 256378-54-8 (Inhibitor n°1), 663203-38-1 (Inhibitor n°2), 247079-73-8 (Inhibitor n°3), 689769-86-6 (Inhibitor n°4) and 431925-096 (Inhibitor n°6) gave positive responses and resulted to be active for Akt/mTOR and/or β-catenin inhibition. This study confirms once again the Molecular Topology’s reliability and efficacy to find out novel drugs in the field of cancer.

What place does molecular topology have in today's drug discovery?

Abstract: Most methods in molecular and drug design are currently based on physicochemical descriptors. However, molecular topology, which relies on topological descriptors, has also shown value for molecula...

Deep-Learning-Based Drug-Target Interaction Prediction.

Abstract: Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug–target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug–drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-a...

Multi-target pharmacology: possibilities and limitations of the “skeleton key approach” from a medicinal chemist perspective

Abstract: Multi-target drugs have raised considerable interest in the last decade owing to their advantages in the treatment of complex diseases and health conditions linked to drug resistance issues. Prospective drug repositioning to treat comorbid conditions is an additional, overlooked application of multi-target ligands. While medicinal chemists usually rely on some version of the lock and key paradigm to design novel therapeutics, modern pharmacology has recognized that the long-term effects of a given drug on a biological system may depend not only on the specific ligand-target recognition events but also on the influence of the chronic administration of a drug on the cell gene signature. The design of multi-target agents also poses challenging restrictions on the either the topology or flexibility of the candidate drugs which are briefly discussed in the present article. Finally, computational strategies to approach the identification of novel multi-target agents are overviewed.

Use of machine learning approaches for novel drug discovery.

Abstract: Introduction: The use of computational tools in the early stages of drug development has increased in recent decades. Machine learning (ML) approaches have been of special interest, since they can ...