The term drug design describes the search of novel compounds with biological activity, on a systematic basis. In its most common form, it involves modification of a known active scaffold or linking known active scaffolds, ...

Multi-target regression (MTR) regards predictive problems with multiple numerical targets. To solve this, machine learning techniques can model solutions treating each target as a separated problem based only on the input ...

Available genomic data for pathogens has created new opportunities for drug discovery and development to fight them, including new resistant and multiresistant strains. In particular structural data must be integrated with ...

The rapid emergence of resistant bacteria makes today's antibiotics more and more ineffective, consequently increasing the need for new pharmacological targets and novel classes of antibacterial drugs. The identification ...

Corynebacterium diphtheriae (Cd) is a gram-positive human pathogen responsible for diphtheria infection and once regarded for high mortalities worldwide. The fatality gradually decreased with improved living standards and ...

In contrast with the reductionist “one gene, one target, one drug” approach, network pharmacology proposes the use of multi-target therapies, a strategy that seems particularly suitable to treat disorders of complex etiology, ...

Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the ...

Background: The increasing number of developmental events and molecular mechanisms associated with the Hedgehog (Hh) pathway from Drosophila to vertebrates, suggest that gene regulation is crucial for diverse cellular ...

A Random Finite Set (RFS) based multi-target filter is proposed, which utilizes a labeled Multi-Bernoulli distribution to model the multi-target state, together with a Poisson RFS distribution to model target birth. Referred ...