New Trends in Virtual Screening

Identification of vilazodone as a novel plasminogen activator inhibitor to overcome Alzheimer's disease through virtual screening, molecular dynamics simulation, and biological evaluation

Urokinase-type plasminogen activator (PLAU), a member of the S1 serine peptidase family in Clan PA, plays a crucial role in the conversion of plasminogen into active plasmin. However, the precise role of PLAU in the central nervous system remains incompletely elucidated, particularly, in relation to Alzheimer's disease (AD). In this study, we successfully identified that PLAU could promote cell senescence in neurons, indicating it as a potential target for AD treatment through a systematic approach, which included both bioinformatics analysis and experimental verification. Subsequently, a structure-based virtual screening approach was employed to identify a potential PLAU inhibitor from the Food and Drug Administration-approved drug database. After analyzing docking scores and thoroughly examining the receptor-ligand complex interaction modes, vilazodone emerges as a highly promising PLAU inhibitor. Additionally, molecular docking and molecular dynamics simulations were performed to generate a complex structure between the relatively stable inhibitor vilazodone and PLAU. Of note, vilazodone exhibited superior cytotoxicity against senescent cells, showing a senolytic activity through targeting PLAU and ultimately producing an anti-AD effect. These findings suggest that targeting PLAU could represent a promising therapeutic strategy for AD. Furthermore, investigating the inhibitory potential and structural modifications based on vilazodone may provide valuable insights for future drug development targeting PLAU in AD disorders.

Antimalarial drugs: what's new in the patents?

INTRODUCTION

The efficacy of current therapeutic warheads in preventing malaria transmission or treating the disease is often hampered by the emergence of drug-resistance. No effective vaccines are available to date, and novel drugs able to counteract drug-resistant forms of malaria and/or to target multiple stages of the parasite's lifecycle are urgently needed.

AREAS COVERED

This review covers patents that protect antimalarial small molecules bearing the artemisinin or other chemical scaffolds, as well as vaccines, that have been published in the period 2015-2022. Literature was searched in public databases of articles and patents. Patents protecting small molecules that prevent malaria transmission are not discussed herein.

EXPERT OPINION

Significant progress has been made in the design of antimalarial agents. Most of these candidates have been tested in standardized strains, with the use of Plasmodium clinical isolates for testing still underdeveloped. Several compounds have been profiled in in vivo mouse models of malaria, including humanised mice. Despite having different efficacy, these new molecules might further progress the field and hopefully will advance to clinical development soon.

Discovery of novel 2,8-diazaspiro[4.5]decan-1-one derivatives as potent RIPK1 kinase inhibitors

Necroptosis, a key form of programmed lytic cell death, has gained recognition as an important driver in various inflammatory diseases. Inhibition of kinase activity of receptor interaction protein kinase 1 (RIPK1), which block the activation of the necroptosis pathway has shown therapeutic potential in many human diseases. In order to find new chemotypes of RIPK1 inhibitors, a virtual screening workflow was performed and led to the discovery of 8-benzoyl-3-benzyl-1,3,8-triazaspiro[4.5]decane-2,4-dione (compound 8) as a hit compound. Further structural optimization identified a series of 2,8-diazaspiro[4.5]decan-1-one derivatives as potent RIPK1 inhibitors. Among them, compound 41 exhibited prominent inhibitory activity against RIPK1 with an IC50 value of 92 nM. Meanwhile, compound 41 showed a significant anti-necroptotic effect in a necroptosis model in U937 cells. Therefore, compound 41 could be employed as a lead compound of RIPK1 inhibitors for further structural optimization.

Identification of fosaprepitant as a novel GSTP1 inhibitor through structure-based virtual screening, molecular dynamics simulation, and biological evaluation

The workflow of virtual screening for the discovery of GSTP1 inhibitors.

Anti-MRSA drug discovery by ligand-based virtual screening and biological evaluation

S. aureus resistant to methicillin (MRSA) is one of the most-concerned multidrug resistant bacteria, due to its role in life-threatening infections. There is an urgent need to develop new antibiotics against MRSA. In this study, we firstly compiled a data set of 2,3-diaminoquinoxalines by chemical synthesis and antibacterial screening against S. aureus, and then performed cheminformatics modeling and virtual screening. The compound with the Specs ID of AG-205/33156020 was discovered as a new antibacterial agent, and was further identified as a Gyrase B (GyrB) inhibitor. In light of the common features, we hypothesized that the 6c as the representative of 2,3-diaminoquinoxalines also inhibited GyrB and eventually proved it. Via molecular docking and molecular dynamics simulations, we identified binding modes of AG-205/33156020 and 6c to the ATPase domain of GyrB. Importantly, these GyrB inhibitors inhibited the MRSA strains and showed selectivity to HepG2 and HUVEC. Taken together, this research work provides an effective ligand-based computational workflow for scaffold hopping in anti-MRSA drug discovery, and discovers two new GyrB inhibitors that are worthy of further development.

Ranking Molecules with Vanishing Kernels and a Single Parameter: Active Applicability Domain Included

In ligand-based virtual screening, high-throughput screening (HTS) data sets can be exploited to train classification models. Such models can be used to prioritize yet untested molecules, from the most likely active (against a protein target of interest) to the least likely active. In this study, a single-parameter ranking method with an Applicability Domain (AD) is proposed. In effect, Kernel Density Estimates (KDE) are revisited to improve their computational efficiency and incorporate an AD. Two modifications are proposed: (i) using vanishing kernels (i.e., kernel functions with a finite support) and (ii) using the Tanimoto distance between molecular fingerprints as a radial basis function. This construction is termed "Vanishing Ranking Kernels" (VRK). Using VRK on 21 HTS assays, it is shown that VRK can compete in performance with a graph convolutional deep neural network. VRK are conceptually simple and fast to train. During training, they require optimizing a single parameter. A trained VRK model usually defines an active AD. Exploiting this AD can significantly increase the screening frequency of a VRK model. Software: https://github.com/UnixJunkie/rankers. Data sets: https://zenodo.org/record/1320776 and https://zenodo.org/record/3540423.

The Synthesizability of Molecules Proposed by Generative Models

The discovery of functional molecules is an expensive and time-consuming process, exemplified by the rising costs of small molecule therapeutic discovery. One class of techniques of growing interest for early stage drug discovery is de novo molecular generation and optimization, catalyzed by the development of new deep learning approaches. These techniques can suggest novel molecular structures intended to maximize a multiobjective function, e.g., suitability as a therapeutic against a particular target, without relying on brute-force exploration of a chemical space. However, the utility of these approaches is stymied by ignorance of synthesizability. To highlight the severity of this issue, we use a data-driven computer-aided synthesis planning program to quantify how often molecules proposed by state-of-the-art generative models cannot be readily synthesized. Our analysis demonstrates that there are several tasks for which these models generate unrealistic molecular structures despite performing well on popular quantitative benchmarks. Synthetic complexity heuristics can successfully bias generation toward synthetically tractable chemical space, although doing so necessarily detracts from the primary objective. This analysis suggests that to improve the utility of these models in real discovery workflows, new algorithm development is warranted.

Natural allosteric modulators and their biological targets: molecular signatures and mechanisms

Covering: 2008 to 2018Over the last decade more than two hundred single natural products were confirmed as natural allosteric modulators (alloNPs) of proteins. The compounds are presented and discussed with the support of a chemical space, constructed using a principal component analysis (PCA) of molecular descriptors from chemical compounds of distinct databases. This analysis showed that alloNPs are dispersed throughout the majority of the chemical space defined by natural products in general. Moreover, a cluster of alloNPs was shown to occupy a region almost devoid of allosteric modulators retrieved from a dataset composed mainly of synthetic compounds, further highlighting the importance to explore the entire natural chemical space for probing allosteric mechanisms. The protein targets which alloNPs bind to comprised 81 different proteins, which were classified into 5 major groups, with enzymes, in particular hydrolases, being the main representative group. The review also brings a critical interpretation on the mechanisms by which alloNPs display their molecular action on proteins. In the latter analysis, alloNPs were classified according to their final effect on the target protein, resulting in 3 major categories: (i) local alteration of the orthosteric site; (ii) global alteration in protein dynamics that change function; and (iii) oligomer stabilisation or protein complex destabilisation via protein-protein interaction in sites distant from the orthosteric site. G-protein coupled receptors (GPCRs), which use a combination of the three types of allosteric regulation found, were also probed by natural products. In summary, the natural allosteric modulators reviewed herein emphasise their importance for exploring alternative chemotherapeutic strategies, potentially pushing the boundaries of the druggable space of pharmacologically relevant drug targets.