Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures

Deep eutectic solvents (DES) are often regarded as greener sustainable alternative solvents and are currently employed in many industrial applications on a large scale. Bearing in mind the industrial importance of DES—and because the vast majority of DES has yet to be synthesized—the development of cheminformatic models and tools efficiently profiling their density becomes essential. In this work, after rigorous validation, quantitative structure-property relationship (QSPR) models were proposed for use in estimating the density of a wide variety of DES. These models were based on a modelling dataset previously employed for constructing thermodynamic models for the same endpoint. The best QSPR models were robust and sound, performing well on an external validation set (set up with recently reported experimental density data of DES). Furthermore, the results revealed structural features that could play crucial roles in ruling DES density. Then, intelligent consensus prediction was employed to develop a consensus model with improved predictive accuracy. All models were derived using publicly available tools to facilitate easy reproducibility of the proposed methodology. Future work may involve setting up reliable, interpretable cheminformatic models for other thermodynamic properties of DES and guiding the design of these solvents for applications.

THE CONSENSUS PREDICTION IN SILICO OF PHARMACOKINETIC PREFERENCE OF MULTI-TARGET RAGE INHIBITORS

Using a neural network model based on docking, among 87 new synthesized substances of ten structurally diverse chemical classes, ten compounds with predicted high RAGE-inhibitory activity were found, and for these by means of Qik Prop, PASS programs and on-line resources admetSAR, pkCSM, SwissADME and ADMET-PreServ a consensus in silico estimation of 14 pharmacokinetic ADMET characteristics was carried out. Based on these indicators, consensus integral estimates of pharmacokinetic preferences of these compounds were calculated and substances with favorable pharmacokinetic properties were identified.

Probabilistic Approach for Virtual Screening Based on Multiple Pharmacophores

Pharmacophore modeling is usually considered as a special type of virtual screening without probabilistic nature. Correspondence of at least one conformation of a molecule to pharmacophore is considered as evidence of its bioactivity. We show that pharmacophores can be treated as one-class machine learning models, and the probability the reflecting model’s confidence can be assigned to a pharmacophore on the basis of their precision of active compounds identification on a calibration set. Two schemes (Max and Mean) of probability calculation for consensus prediction based on individual pharmacophore models were proposed. Both approaches to some extent correspond to commonly used consensus approaches like the common hit approach or the one based on a logical OR operation uniting hit lists of individual models. Unlike some known approaches, the proposed ones can rank compounds retrieved by multiple models. These approaches were benchmarked on multiple ChEMBL datasets used for ligand-based pharmacophore modeling and externally validated on corresponding DUD-E datasets. The influence of complexity of pharmacophores and their performance on a calibration set on results of virtual screening was analyzed. It was shown that Max and Mean approaches have superior early enrichment to the commonly used approaches. Thus, a well-performing, easy-to-implement, and probabilistic alternative to existing approaches for pharmacophore-based virtual screening was proposed.

GeDex: A consensus Gene-disease Event Extraction System based on frequency patterns and supervised learning

MotivationThe genetic mechanisms involved in human diseases are fundamental in biomedical research. Several databases with curated associations between genes and diseases have emerged in the last decades. Although, due to the demanding and time consuming nature of manual curation of literature, they still lack large amounts of information. Current automatic approaches extract associations by considering each abstract or sentence independently. This approach could potentially lead to contradictions between individual cases. Therefore, there is a current need for automatic strategies that can provide a literature consensus of gene-disease associations, and are not prone to making contradictory predictions.ResultsHere, we present GeDex, an effective and freely available automatic approach to extract consensus gene-disease associations from biomedical literature based on a predictive model trained with four simple features. As far as we know, it is the only system that reports a single consensus prediction from multiple sentences supporting the same association. We tested our approach on the curated fraction of DisGeNet (f-score 0.77) and validated it on a manually curated dataset, obtaining a competitive performance when compared to pre-existing methods (f-score 0.74). In addition, we effectively recovered associations from an article collection of chronic pulmonary diseases, and discovered that a large proportion is not reported in current databases. Our results demonstrate that GeDex, despite its simplicity, is a competitive tool that can successfully assist the curation of existing databases.AvailabilityGeDex is available at https://bitbucket.org/laigen/gedex/src/master/ and can be used as a docker image https://hub.docker.com/r/laigen/[email protected] informationSupplementary material are available at bioRxiv online.

INGA 2.0: improving protein function prediction for the dark proteome

Our current knowledge of complex biological systems is stored in a computable form through the Gene Ontology (GO) which provides a comprehensive description of genes function. Prediction of GO terms from the sequence remains, however, a challenging task, which is particularly critical for novel genomes. Here we present INGA 2.0, a new version of the INGA software for protein function prediction. INGA exploits homology, domain architecture, interaction networks and information from the ‘dark proteome’, like transmembrane and intrinsically disordered regions, to generate a consensus prediction. INGA was ranked in the top ten methods on both CAFA2 and CAFA3 blind tests. The new algorithm can process entire genomes in a few hours or even less when additional input files are provided. The new interface provides a better user experience by integrating filters and widgets to explore the graph structure of the predicted terms. The INGA web server, databases and benchmarking are available from URL: https://inga.bio.unipd.it/.