Multiple approaches to repurposing drugs for neuroblastoma.

Neuroblastoma (NB) is the second leading extracranial solid tumor of early childhood with about two-thirds of cases presenting before the age of 5, and accounts for roughly 15 percent of all pediatric cancer fatalities in the United States. Treatments against NB are lacking, resulting in a low survival rate in high-risk patients. A repurposing approach using already approved or clinical stage compounds can be used for diseases for which the patient population is small, and the commercial market limited.

Chalcones from Angelica keiskei (ashitaba) inhibit key Zika virus replication proteins.

Zika virus (ZIKV) is a dangerous human pathogen and no antiviral drugs have been approved to date. The chalcones are a group of small molecules that are found in a number of different plants, including Angelica keiskei Koidzumi, also known as ashitaba. To examine chalcone anti-ZIKV activity, three chalcones, 4-hydroxyderricin (4HD), xanthoangelol (XA), and xanthoangelol-E (XA-E), were purified from a methanol-ethyl acetate extract from A.

Mycobacterium abscessus drug discovery using machine learning.

The prevalence of infections by nontuberculous mycobacteria is increasing, having surpassed tuberculosis in the United States and much of the developed world. Nontuberculous mycobacteria occur naturally in the environment and are a significant problem for patients with underlying lung diseases such as bronchiectasis, chronic obstructive pulmonary disease, and cystic fibrosis. Current treatment regimens are lengthy, complicated, toxic and they are often unsuccessful as seen by disease recurrence.

Remdesivir and EIDD-1931 Interact with Human Equilibrative Nucleoside Transporters 1 and 2: Implications for Reaching SARS-CoV-2 Viral Sanctuary Sites.

Equilibrative nucleoside transporters (ENTs) are present at the blood-testis barrier (BTB), where they can facilitate antiviral drug disposition to eliminate a sanctuary site for viruses detectable in semen. The purpose of this study was to investigate ENT-drug interactions with three nucleoside analogs, remdesivir, molnupiravir, and molnupiravir's active metabolite, -d-N-hydroxycytidine (EIDD-1931), and four non-nucleoside molecules repurposed as antivirals for coronavirus disease 2019 (COVID-19). The study used three-dimensional pharmacophores for ENT1 and ENT2 substrates and inhibitors and Bayesian machine learning models to identify potential interactions with these transporters.

Machine Learning Models Identify Inhibitors of SARS-CoV-2.

With the rapidly evolving SARS-CoV-2 variants of concern, there is an urgent need for the discovery of further treatments for the coronavirus disease (COVID-19). Drug repurposing is one of the most rapid strategies for addressing this need, and numerous compounds have already been selected for testing by several groups. These have led to a growing database of molecules with activity against the virus.

Development of Machine Learning Models and the Discovery of a New Antiviral Compound against Yellow Fever Virus.

Yellow fever (YF) is an acute viral hemorrhagic disease transmitted by infected mosquitoes. Large epidemics of YF occur when the virus is introduced into heavily populated areas with high mosquito density and low vaccination coverage. The lack of a specific small molecule drug treatment against YF as well as for homologous infections, such as zika and dengue, highlights the importance of these flaviviruses as a public health concern.

Cationic Compounds with SARS-CoV-2 Antiviral Activity and Their Interaction with Organic Cation Transporter/Multidrug and Toxin Extruder Secretory Transporters.

In the wake of the COVID-19 pandemic, drug repurposing has been highlighted for rapid introduction of therapeutics. Proposed drugs with activity against SARS-CoV-2 include compounds with positive charges at physiologic pH, making them potential targets for the organic cation secretory transporters of kidney and liver, i.e.

Correction to "Using Bibliometric Analysis and Machine Learning to Identify Compounds Binding to Sialidase-1".

[This corrects the article DOI: 10.1021/acsomega.0c05591.

Quantum Machine Learning Algorithms for Drug Discovery Applications.

The growing quantity of public and private data sets focused on small molecules screened against biological targets or whole organisms provides a wealth of drug discovery relevant data. This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large data sets with thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations.

Comparing the Pfizer Central Nervous System Multiparameter Optimization Calculator and a BBB Machine Learning Model.

The ability to calculate whether small molecules will cross the blood-brain barrier (BBB) is an important task for companies working in neuroscience drug discovery. For a decade, scientists have relied on relatively simplistic rules such as Pfizer's central nervous system multiparameter optimization models (CNS-MPO) for guidance during the drug selection process. In parallel, there has been a continued development of more sophisticated machine learning models that utilize different molecular descriptors and algorithms; however, these models represent a "black box" and are generally less interpretable.

CATMoS: Collaborative Acute Toxicity Modeling Suite.

Background: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests.

Flavonoids from Pterogyne nitens as Zika virus NS2B-NS3 protease inhibitors.

Although the widespread epidemic of Zika virus (ZIKV) and its neurological complications are well-known there are still no approved drugs available to treat this arboviral disease or vaccine to prevent the infection. Flavonoids from Pterogyne nitens have already demonstrated anti-flavivirus activity, although their target is unknown. In this study, we virtually screened an in-house database of 150 natural and semi-synthetic compounds against ZIKV NS2B-NS3 protease (NS2B-NS3p) using docking-based virtual screening, as part of the OpenZika project.

Using Bibliometric Analysis and Machine Learning to Identify Compounds Binding to Sialidase-1.

Rare diseases impact hundreds of millions of individuals worldwide. However, few therapies exist to treat the rare disease population because financial resources are limited, the number of patients affected is low, bioactivity data is often nonexistent, and very few animal models exist to support preclinical development efforts. Sialidosis is an ultrarare lysosomal storage disorder in which mutations in the NEU1 gene result in the deficiency of the lysosomal enzyme sialidase-1.

A Machine Learning Strategy for Drug Discovery Identifies Anti-Schistosomal Small Molecules.

Schistosomiasis is a chronic and painful disease of poverty caused by the flatworm parasite . Drug discovery for antischistosomal compounds predominantly employs whole organism (phenotypic) screens against two developmental stages of , post-infective larvae (somules) and adults. We generated two rule books and associated scoring systems to normalize 3898 phenotypic data points to enable machine learning.

Bioactivity Comparison across Multiple Machine Learning Algorithms Using over 5000 Datasets for Drug Discovery.

Machine learning methods are attracting considerable attention from the pharmaceutical industry for use in drug discovery and applications beyond. In recent studies, we and others have applied multiple machine learning algorithms and modeling metrics and, in some cases, compared molecular descriptors to build models for individual targets or properties on a relatively small scale. Several research groups have used large numbers of datasets from public databases such as ChEMBL in order to evaluate machine learning methods of interest to them.

Predicting Drug Interactions with Human Equilibrative Nucleoside Transporters 1 and 2 Using Functional Knockout Cell Lines and Bayesian Modeling.

Equilibrative nucleoside transporters (ENTs) 1 and 2 facilitate nucleoside transport across the blood-testis barrier (BTB). Improving drug entry into the testes with drugs that use endogenous transport pathways may lead to more effective treatments for diseases within the reproductive tract. In this study, CRISPR/CRISPR-associated protein 9 was used to generate HeLa cell lines in which ENT expression was limited to ENT1 or ENT2.

Computational Approaches to Identify Molecules Binding to KasA.

Tuberculosis is caused by and is a deadly disease resulting in the deaths of approximately 1.5 million people with 10 million infections reported in 2018. Recently, a key condensation step in the synthesis of mycolic acids was shown to require β-ketoacyl-ACP synthase (KasA).

Dispirotripiperazine-core compounds, their biological activity with a focus on broad antiviral property, and perspectives in drug design (mini-review).

Viruses are obligate intracellular parasites and have evolved to enter the host cell. To gain access they come into contact with the host cell through an initial adhesion, and some viruses from different genus may use heparan sulfate proteoglycans for it. The successful inhibition of this early event of the infection by synthetic molecules has always been an attractive target for medicinal chemists.

Comparing Machine Learning Models for Aromatase (P450 19A1).

Aromatase, or cytochrome P450 19A1, catalyzes the aromatization of androgens to estrogens within the body. Changes in the activity of this enzyme can produce hormonal imbalances that can be detrimental to sexual and skeletal development. Inhibition of this enzyme can occur with drugs and natural products as well as environmental chemicals.