Screening and computational characterization of novel antimicrobial cathelicidins from amphibian transcriptomic data.

As cold-blooded organisms living in damp and dark environments, amphibians have evolved robust defense mechanisms to protect themselves from predators and infections. Among the wide repertoire of bioactive compounds they produce are antimicrobial peptides (AMPs), which are required as part of innate immunity. One important class of AMPs is cathelicidins, known for their broad-spectrum activity against pathogens and their immunoregulatory roles.

MolGC: molecular geometry comparator algorithm for bond length mean absolute error computation on molecules.

Density Functional Theory (DFT) is extensively used in theoretical and computational chemistry to study molecular and crystal properties across diverse fields, including quantum chemistry, materials physics, catalysis, biochemistry, and surface science. Despite advances in DFT hardware and software for optimized geometries, achieving consensus in molecular structure comparisons with experimental counterparts remains a challenge. This difficulty is exacerbated by the lack of automated bond length comparison tools, resulting in labor-intensive and error-prone manual processes.

Copper complex molecules as dye-sensitizers: Hybrid MetaGGA and standard + van der Waals functionals.

This study focuses on the use of Density Functional Theory calculations with two main approaches: computational chemistry and computational physics. The following three cases were considered for the derivation: (I) computational chemistry using the M06 hybrid functional, (II) computational chemistry using the standard PBE functional including vdW interactions, and (III) computational physics using the standard PBE functional including vdW interactions and periodic boundary conditions. Since the approximation using hybrid functionals M06 has been extensively validated, this method was used as a reference.

Deep learning algorithms applied to computational chemistry.

Recently, there has been a significant increase in the use of deep learning techniques in the molecular sciences, which have shown high performance on datasets and the ability to generalize across data. However, no model has achieved perfect performance in solving all problems, and the pros and cons of each approach remain unclear to those new to the field. Therefore, this paper aims to review deep learning algorithms that have been applied to solve molecular challenges in computational chemistry.

DeepFoveaNet: Deep Fovea Eagle-Eye Bioinspired Model to Detect Moving Objects.

Birds of prey especially eagles and hawks have a visual acuity two to five times better than humans. Among the peculiar characteristics of their biological vision are that they have two types of foveae; one shallow fovea used in their binocular vision, and a deep fovea for monocular vision. The deep fovea allows these birds to see objects at long distances and to identify them as possible prey.