Virtual Screening Strategy Combined Bayesian Classification Model, Molecular Docking for Acetyl-CoA Carboxylases Inhibitors

Machine-Learning-Assisted Nanozyme Design: Lessons from Materials and Engineered Enzymes

Nanozymes are nanomaterials that exhibit enzyme-like biomimicry. In combination with intrinsic characteristics of nanomaterials, nanozymes have broad applicability in materials science, chemical engineering, bioengineering, biochemistry, and disease theranostics. Recently, the heterogeneity of published results has highlighted the complexity and diversity of nanozymes in terms of consistency of catalytic capacity. Machine learning (ML) shows promising potential for discovering new materials, yet it remains challenging for the design of new nanozymes based on ML approaches. Alternatively, ML is employed to promote optimization of intelligent design and application of catalytic materials and engineered enzymes. Incorporation of the successful ML algorithms used in the intelligent design of catalytic materials and engineered enzymes can concomitantly facilitate the guided development of next-generation nanozymes with desirable properties. Here, recent progress in ML, its utilization in the design of catalytic materials and enzymes, and how emergent ML applications serve as promising strategies to circumvent challenges associated with time-expensive and laborious testing in nanozyme research and development are summarized. The potential applications of successful examples of ML-aided catalytic materials and engineered enzymes in nanozyme design are also highlighted, with special focus on the unified aims in enhancing design and recapitulation of substrate selectivity and catalytic activity.

Vortioxetine liposomes as a novel alternative to improve drug stability under stress conditions: toxicity studies and evaluation of antidepressant-like effect

BACKGROUND

Vortioxetine hydrobromide (VXT), a new therapeutic option in the treatment of major depressive disorder, is a poorly soluble drug, and instability under stress conditions has been reported. The aim of the present study was to prepare VXT liposomes (VXT-Ls) with an antidepressant-like effect, to improve drug stability and reduce toxicity of the free drug.

METHODS

Liposomes were prepared using the thin lipid film hydration method and properly characterized. Forced degradation studies were conducted in photolytic and oxidative conditions. The cytotoxicity was evaluated in VERO cells through MTT assay and in vivo toxicity was assessed in mice. The antidepressant-like effect in mice was confirmed using the open-field test paradigm and tail suspension test.

RESULTS

The optimized VXT-Ls have multilamellar vesicles with an average size of 176.74 nm ± 2.43. The liposomal formulation increased the stability of VXT. VERO cell viability was maintained at around 40% when the VXT-Ls were tested at higher concentrations and no signs of acute toxicity were observed in mice. The antidepressant-like effect was effective, for VXT-Ls, at doses ranging from 2.5 mg/kg to 10 mg/kg, measured by the tail suspension test in mice. The non-liposomal formulation was effective at a dose of 10 mg/kg. The open field test was performed and any unspecific changes in locomotor activity were revealed.

CONCLUSIONS

Liposomes seem to be a promising alternative for an oral VXT formulation at lower doses (2.5 mg/kg).

Artificial intelligence in virtual screening: models versus experiments

A typical drug discovery project involves identifying active compounds with significant binding potential for selected disease-specific targets. Experimental high-throughput screening (HTS) is a traditional approach to drug discovery, but is expensive and time-consuming when dealing with huge chemical libraries with billions of compounds. The search space can be narrowed down with the use of reliable computational screening approaches. In this review, we focus on various machine-learning (ML) and deep-learning (DL)-based scoring functions developed for solving classification and ranking problems in drug discovery. We highlight studies in which ML and DL models were successfully deployed to identify lead compounds for which the experimental validations are available from bioassay studies.

A multi-conformational virtual screening approach based on machine learning targeting PI3Kγ

Nowadays, more and more attention has been attracted to develop selective PI3Kγ inhibitors, but the unique structural features of PI3Kγ protein make it a very big challenge. In the present study, a virtual screening strategy based on machine learning with multiple PI3Kγ protein structures was developed to screen novel PI3Kγ inhibitors. First, six mainstream docking programs were chosen to evaluate their scoring power and screening power; CDOCKER and Glide show satisfactory reliability and accuracy against the PI3Kγ system. Next, virtual screening integrating multiple PI3Kγ protein structures was demonstrated to significantly improve the screening enrichment rate comparing to that with an individual protein structure. Last, a multi-conformational Naïve Bayesian Classification model with the optimal docking programs was constructed, and it performed a true capability in the screening of PI3Kγ inhibitors. Taken together, the current study could provide some guidance for the docking-based virtual screening to discover novel PI3Kγ inhibitors.