pharmACOphore: multiple flexible ligand alignment based on ant colony optimization

info:eu-repo/semantics/publishe

Artificial Intelligence in Drug Design

Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared and underpin the strength of this technology in quantitative structure-property relationships (QSPR) or quantitative structure-activity relationships (QSAR). Artificial intelligence in de novo design drives the generation of meaningful new biologically active molecules towards desired properties. Several examples establish the strength of artificial intelligence in this field. Combination with synthesis planning and ease of synthesis is feasible and more and more automated drug discovery by computers is expected in the near future

Artificial Intelligence in Drug Design

Artificial Intelligence (AI) plays a pivotal role in drug discovery. In particular artificial neural networks such as deep neural networks or recurrent networks drive this area. Numerous applications in property or activity predictions like physicochemical and ADMET properties have recently appeared and underpin the strength of this technology in quantitative structure-property relationships (QSPR) or quantitative structure-activity relationships (QSAR). Artificial intelligence in de novo design drives the generation of meaningful new biologically active molecules towards desired properties. Several examples establish the strength of artificial intelligence in this field. Combination with synthesis planning and ease of synthesis is feasible and more and more automated drug discovery by computers is expected in the near future

Fractal Dimensions of Macromolecular Structures

Quantifying the properties of macromolecules is a prerequisite for understanding their roles in biochemical processes. One of the less-explored geometric features of macromolecules is molecular surface irregularity, or ‘roughness’, which can be measured in terms of fractal dimension (D). In this study, we demonstrate that surface roughness correlates with ligand binding potential. We quantified the surface roughnesses of biological macromolecules in a large-scale survey that revealed D values between 2.0 and 2.4. The results of our study imply that surface patches involved in molecular interactions, such as ligand-binding pockets and protein-protein interfaces, exhibit greater local fluctuations in their fractal dimensions than ‘inert’ surface areas. We expect approximately 22 % of a protein’s surface outside of the crystallographically known ligand binding sites to be ligandable. These findings provide a fresh perspective on macromolecular structure and have considerable implications for drug design as well as chemical and systems biology.ISSN:1868-1743ISSN:1868-175

In vivo evaluation of antinociceptive effects of cyriotoxin-1a, the first toxin purified fromCyriopagopus schioedtei spider venom

International audienceOver the past two decades, animal venom toxins have been widely explored as an original source of new antinociceptive drugs directed towards the Nav1.7 subtype of voltage-gated sodium channels. This subtype, expressed in afferent sensitive fibers and more particularly in dorsal root ganglia (DRG) neurons, has been validated as an antinociceptive target of choice by human genetic evidence such as congenital insensivity to pain [1]. High throughput screening of Smartox biotechnology company collection venoms, using automated patch-clamp (QPatch HTX, Sophion Biosciences) on HEK cells overexpressing human Nav subtypes, pointed out a new inhibitory cysteine knot (ICK) toxin, named cyriotoxin-1a (CyrTx-1a). In DRG neurons isolated from adult mice, the peptide preferentially inhibited with high affinity tetrodotoxin (TTX)-sensitive Na current (IC50 = 170 nM), flowing through mainly the Nav1.7 subtype, compared to TTX-resistant Na current (IC50 =108 μM), flowing through Nav1.8 and Nav1.9 subtypes. In addition, the peptide exhibited nanomolar range affinity for Nav1.7, Nav1.1-1.3 and NaV1.6 subtypes and micromolar range affinity for Nav1.5 and Nav1.4 subtypes. The hot plate and von Frey pain assays showed that mice injected with 367 μg/kg of CyrTx-1a were more resistant to pain than animals injected with the vehicle (PBS). However, compound muscle action potential (CMAP) recordings after toxin injection revealed a relative narrow therapeutic window w/o side-effects. In conclusion, the pharmacological profile of CyrTx-1a is of great interest since it leads to further engineering studies aimed to improve its selectivity for optimizing the use of this peptide as an antinociceptive therapeutics.[1].Vetter et al. (2016) Nav1.7 as a pain target – from gene to pharmacology. Pharmacology and Therapeutics 172, 73-100