Distinct populations suppress or escalate intake of cocaine paired with aversive quinine.

Background: Only a subset of individuals who encounter illicit drugs become persons with a substance use disorder. Individual differences in aversive reactions to drug-associated phenomena like smoke inhalation and unpleasant taste are predictors for continued use. While several preclinical studies have explored self-administration involving aversive cues, none have simultaneously introduced aversion with the initial drug self-administration.

Distinct populations suppress or escalate intake of cocaine paired with aversive quinine.

Background: Only a subset of individuals who encounter drugs of abuse become habitual users. Aversive subjective effects like coughing and unpleasant taste are predictors for continued use. While several preclinical studies have explored self-administration involving aversive cues, none have simultaneously introduced aversion with the initial drug self-administration.

SynRoute: A Retrosynthetic Planning Software.

Computer-assisted synthetic planning has seen major advancements that stem from the availability of large reaction databases and artificial intelligence methodologies. SynRoute is a new retrosynthetic planning software tool that uses a relatively small number of general reaction templates, currently 263, along with a literature-based reaction database to find short, practical synthetic routes for target compounds. For each reaction template, a machine learning classifier is trained using data from the Pistachio reaction database to predict whether new computer-generated reactions based on the template are likely to work experimentally in the laboratory.

Machine Learning Models for Predicting Molecular UV-Vis Spectra with Quantum Mechanical Properties.

Accurate understanding of ultraviolet-visible (UV-vis) spectra is critical for the high-throughput synthesis of compounds for drug discovery. Experimentally determining UV-vis spectra can become expensive when dealing with a large quantity of novel compounds. This provides us an opportunity to drive computational advances in molecular property predictions using quantum mechanics and machine learning methods.

Promoting the Participation of "Hard-to-Reach" Migrant Populations in Qualitative Public Health Research during the COVID-19 Pandemic in Chile.

The COVID-19 pandemic has further deepened socioeconomic and health inequities worldwide, especially among populations experiencing social vulnerability, such as international migrants. Sustained lockdowns and social distancing have raised challenges to conducting public health research with hard-to-reach populations. This study aims at exploring strategies to recruit "hard-to-reach" international migrants for qualitative public health research during the pandemic in Chile, based on the authors' experience.

Accelerating space radiation countermeasure development through drug repurposing.

The discovery of safe and effective radiation countermeasures (MCM) for long-duration spaceflight is challenging due to the complexity of the space radiation biology and high safety requirements. There are few if any clinically-validated molecular targets for this use case, and preclinical models have several known limitations. These challenges make the evaluation of existing FDA-approved drugs for this indication, or drug repurposing, an attractive strategy to accelerate space radiation countermeasure development.

Mega-High-Throughput Screening Platform for the Discovery of Biologically Relevant Sequence-Defined Non-Natural Polymers.

Combinatorial methods enable the synthesis of chemical libraries on scales of millions to billions of compounds, but the ability to efficiently screen and sequence such large libraries has remained a major bottleneck for molecular discovery. We developed a novel technology for screening and sequencing libraries of synthetic molecules of up to a billion compounds in size. This platform utilizes the fiber-optic array scanning technology (FAST) to screen bead-based libraries of synthetic compounds at a rate of 5 million compounds per minute (∼83 000 Hz).

UV-adVISor: Attention-Based Recurrent Neural Networks to Predict UV-Vis Spectra.

Ultraviolet-visible (UV-Vis) absorption spectra are routinely collected as part of high-performance liquid chromatography (HPLC) analysis systems and can be used to identify chemical reaction products by comparison to the reference spectra. Here, we present UV-adVISor as a new computational tool for predicting the UV-Vis spectra from a molecule's structure alone. UV-Vis prediction was approached as a sequence-to-sequence problem.

Contraceptive and Infertility Target DataBase: a contraceptive drug development tool for targeting and analysis of human reproductive specific tissues†.

The long and challenging drug development process begins with discovery biology for the selection of an appropriate target for a specific indication. Target is a broad term that can be applied to a range of biological entities such as proteins, genes, and ribonucleic acids (RNAs). Although there are numerous databases available for mining biological entities, publicly available searchable, downloadable databases to aid in target selection for a specific disease or indication (e.

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.

Repurposing the Ebola and Marburg Virus Inhibitors Tilorone, Quinacrine, and Pyronaridine: Activity against SARS-CoV-2 and Potential Mechanisms.

Severe acute respiratory coronavirus 2 (SARS-CoV-2) is a newly identified virus that has resulted in over 2.5 million deaths globally and over 116 million cases globally in March, 2021. Small-molecule inhibitors that reverse disease severity have proven difficult to discover.

The Antiviral Drug Tilorone Is a Potent and Selective Inhibitor of Acetylcholinesterase.

Acetylcholinesterase (AChE) is an important drug target in neurological disorders like Alzheimer's disease, Lewy body dementia, and Parkinson's disease dementia as well as for other conditions like myasthenia gravis and anticholinergic poisoning. In this study, we have used a combination of high-throughput screening, machine learning, and docking to identify new inhibitors of this enzyme. Bayesian machine learning models were generated with literature data from ChEMBL for eel and human AChE inhibitors as well as butyrylcholinesterase inhibitors (BuChE) and compared with other machine learning methods.

Repurposing the Ebola and Marburg Virus Inhibitors Tilorone, Quinacrine and Pyronaridine: In vitro Activity Against SARS-CoV-2 and Potential Mechanisms.

SARS-CoV-2 is a newly identified virus that has resulted in over 1.3 M deaths globally and over 59 M cases globally to date. Small molecule inhibitors that reverse disease severity have proven difficult to discover.

Correction to: Tilorone: a Broad-Spectrum Antiviral Invented in the USA and Commercialized in Russia and beyond.

The mentioned Tables below had data in the bottom half (right hand side) of each table that was formatted incorrectly in the final proof version versus the submitted version.

Repurposing Pyramax®, quinacrine and tilorone as treatments for Ebola virus disease.

We have recently identified three molecules (tilorone, quinacrine and pyronaridine tetraphosphate) which all demonstrated efficacy in the mouse model of infection with mouse-adapted Ebola virus (EBOV) model of disease and had similar in vitro inhibition of an Ebola pseudovirus (VSV-EBOV-GP), suggesting they interfere with viral entry. Using a machine learning model to predict lysosomotropism these compounds were evaluated for their ability to possess a lysosomotropic mechanism in vitro. We now demonstrate in vitro that pyronaridine tetraphosphate is an inhibitor of Lysotracker accumulation in lysosomes (IC50 = 0.

Pyronaridine tetraphosphate efficacy against Ebola virus infection in guinea pig.

The recent outbreaks of the Ebola virus (EBOV) in Africa have brought global visibility to the shortage of available therapeutic options to treat patients infected with this or closely related viruses. We have recently computationally identified three molecules which have all demonstrated statistically significant efficacy in the mouse model of infection with mouse adapted Ebola virus (ma-EBOV). One of these molecules is the antimalarial pyronaridine tetraphosphate (IC range of 0.

Tilorone: a Broad-Spectrum Antiviral Invented in the USA and Commercialized in Russia and beyond.

For the last 50 years we have known of a broad-spectrum agent tilorone dihydrochloride (Tilorone). This is a small-molecule orally bioavailable drug that was originally discovered in the USA and is currently used clinically as an antiviral in Russia and the Ukraine. Over the years there have been numerous clinical and non-clinical reports of its broad spectrum of antiviral activity.

Tilorone, a Broad-Spectrum Antiviral for Emerging Viruses.

Tilorone is a 50-year-old synthetic small-molecule compound with antiviral activity that is proposed to induce interferon after oral administration. This drug is used as a broad-spectrum antiviral in several countries of the Russian Federation. We have recently described activity and against the Ebola virus.

Repurposing the antimalarial pyronaridine tetraphosphate to protect against Ebola virus infection.

Recent outbreaks of the Ebola virus (EBOV) have focused attention on the dire need for antivirals to treat these patients. We identified pyronaridine tetraphosphate as a potential candidate as it is an approved drug in the European Union which is currently used in combination with artesunate as a treatment for malaria (EC50 between 420 nM-1.14 μM against EBOV in HeLa cells).

Repurposing Quinacrine against Ebola Virus Infection .

Quinacrine hydrochloride is a small-molecule, orally bioavailable drug that has been used clinically as an antimalarial and for many other applications. A machine learning model trained on Ebola virus (EBOV) screening data identified quinacrine as a potent (nanomolar) inhibitor. In the current study, quinacrine 25 mg/kg was shown to protect 70% of mice (statistically significant) from a lethal challenge with mouse-adapted EBOV with once-daily intraperitoneal dosing for 8 days.

The Natural Product Eugenol Is an Inhibitor of the Ebola Virus In Vitro.

Purpose: Since the 2014 Ebola virus (EBOV) outbreak in West Africa there has been considerable effort towards developing drugs to treat Ebola virus disease and yet to date there is no FDA approved treatment. This is important as at the time of writing this manuscript there is an ongoing outbreak in the Democratic Republic of the Congo which has killed over 1000.

Methods: We have evaluated a small number of natural products, some of which had shown antiviral activity against other pathogens.

Ebola Virus Bayesian Machine Learning Models Enable New in Vitro Leads.

We have previously described the first Bayesian machine learning models from FDA-approved drug screens, for identifying compounds active against the Ebola virus (EBOV). These models led to the identification of three active molecules in vitro: tilorone, pyronaridine, and quinacrine. A follow-up study demonstrated that one of these compounds, tilorone, has 100% in vivo efficacy in mice infected with mouse-adapted EBOV at 30 mg/kg/day intraperitoneal.

Developing a Comprehensive Trauma- and Resiliency- Focused Program After Superstorm Sandy in New York City.

ABSTRACTTo contribute to the ongoing discourse about successful programming supporting intermediate behavioral and mental health needs of vulnerable communities affected by disaster, this article presents the Children's Health Fund (CHF) Sandy Recovery and Resiliency Program as a descriptive case study for a multifaceted, community-based approach to building resiliency, coping, and socioemotional skills in an underserved community in New York City that was affected by Superstorm Sandy. The case study involves retrospective review and analysis of qualitative and quantitative data that were collected as part of routine care and program implementation. From the analysis emerged a program consisting of 3 components: (1) delivery of workshops and community events to decrease stigma and build community-wide resilience, (2) delivery of workshops for students and educators in the local school to increase coping skills as well as referrals to clinical mental health care, and (3) provision of mental health care via a mobile mental health clinic.