TOLEDO: enhancing Maestro GUI for non-expert users to perform massive MD simulations.

Classical Molecular Dynamics (MD) simulates the dynamical evolution of biological systems at the atomic level. Using MD in conjunction with high-performance computing (HPC) architectures, we can evaluate the possible interactions between a ligand library against one protein target to find a drug that can influence a protein target to cure a disease. Simultaneously, we can also obtain information about their dynamic evolution.

ANTIAGE-DB: A Database and Server for the Prediction of Anti-Aging Compounds Targeting Elastase, Hyaluronidase, and Tyrosinase.

Natural products bear a multivariate biochemical profile with antioxidant, anti-inflammatory, antibacterial, and antitumoral properties. Along with their natural sources, they have been widely used both as anti-aging and anti-melanogenic agents due to their effective contribution in the elimination of reactive oxygen species (ROS) caused by oxidative stress. Their anti-aging activity is mainly related to their capacity of inhibiting enzymes like Human Neutrophil Elastase (HNE), Hyaluronidase (Hyal) and Tyrosinase (Tyr).

WADDAICA: A webserver for aiding protein drug design by artificial intelligence and classical algorithm.

Artificial intelligence can train the related known drug data into deep learning models for drug design, while classical algorithms can design drugs through established and predefined procedures. Both deep learning and classical algorithms have their merits for drug design. Here, the webserver WADDAICA is built to employ the advantage of deep learning model and classical algorithms for drug design.

Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey.

Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements.

A review of ligand-based virtual screening web tools and screening algorithms in large molecular databases in the age of big data.

Virtual screening has become a widely used technique for helping in drug discovery processes. The key to this success is its ability to aid in the identification of novel bioactive compounds by screening large molecular databases. Several web servers have emerged in the last few years supplying platforms to guide users in screening publicly accessible chemical databases in a reasonable time.