No abstract

Here we report the development of MolPredictX, an innovate and freely accessible web interface for biological activity predictions of query molecules. MolPredictX utilizes in‐house QSAR models to provide 27 qualitative predictions (active or inactive), and quantitative probabilities for bioactivity against parasitic (Trypanosoma and Leishmania), viral (Dengue, Sars‐CoV and Hepatitis C), pathogenic yeast (Candida albicans), bacterial (Salmonella enterica and Escherichia coli), and Alzheimer disease enzymes. In this article, we introduce the methodology and usability of this webtool, highlighting its potential role in the development of new drugs against a variety of diseases. MolPredictX is undergoing continuous development and is freely available at https://www.molpredictx.ufpb.br/.

Coronaviruses are a class of single-stranded, positive-sense RNA viruses that have caused three notable outbreaks over the past two decades: Middle East respiratory syndrome–related coronavirus (MERS-CoV), severe acute respiratory syndrome coronavirus (SARS-CoV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). All outbreaks have been associated with significant morbidity and mortality. In this study, we hypothesized that conserved binding sites in the key coronavirus proteins can be explored for the development of broad-spectrum direct acting anti-coronaviral compounds, identified such conserved binding site residues across coronaviruses, and validated our hypotheses with existing experimental data. We have identified four coronaviral proteins with highly conserved binding site sequence and 3D structure similarity: PLpro, Mpro, nsp10-nsp16 complex(methyltransferase), and nsp15 endoribonuclease. We have compiled all available experimental data for known antiviral medications inhibiting these targets and identified compounds active against multiple coronaviruses. The identified compounds representing potential broad-spectrum antivirals include: GC376, which is active against six viral Mpro (out of six tested, as described in research literature); mycophenolic acid, which is active against four viral PLpro (out of four); and emetine, which is active against four viral RdRp (out of four). The approach described in this study for coronaviruses, which combines the assessment of sequence and structure conservation across a viral family with the analysis of accessible chemical structure – antiviral activity data, can be explored for the development of broad-spectrum drugs for multiple viral families.

Computational models that predict PK properties, such as those related to drug absorption, metabolism, distribution, and excretion, are critical to flagging drug candidates with poor PK profiles that emerge as hits in high-throughput screening campaigns. To support the development of reliable computational models to predict key PK properties, we collected, curated, and integrated a database of compounds tested in 13 major PK endpoints containing over 10,000 unique molecules. We built classification quantitative structure-activity relationship (QSAR) models for all but one endpoint (Cmax) following best practices of model development and validation. Those with acceptable external accuracy (CCR ≥ 0.60 and SE, PPV, SP, and NPV ≥ 0.50) include hepatic stability at 15, 30, and 60 minutes, hepatic half-life at the subcellular and tissue levels, renal clearance, blood brain barrier permeability, CNS activity, Caco-2 permeability, plasma protein binding, plasma half-life, microsomal intrinsic clearance, and oral bioavailability. As a case study to illustrate model utility, we employed all developed models to predict the PK properties of all compounds in DrugBank. We also predicted PK properties of molecules hitting popular drug targets among several organs SLC6A4 (brain), ADRB2 (heart and lungs), HMGCR (liver), and CaSR (kidneys) only. These analyses revealed that nearly all experimental, investigational, and withdrawn compounds included in DrugBank are hepatically stable at 60 minutes and under, exhibit CNS activity, and permeate the Caco-2 cell line (a measure of intestinal absorption). Furthermore, our results indicate that compounds targeting different organs have distinct predicted PK profiles. This observation suggests that desired PK properties depend on compound’s indication. All models described in this paper have been integrated and made publicly available via the novel predictor of pharmacokinetic properties (PhaKinPro) web portal that can be accessed at https://phakinpro.mml.unc.edu/.