bbSelect - An Open-Source Tool for Performing a 3D Pharmacophore-Driven Diverse Selection of R-Groups.

The design of compounds during hit-to-lead often seeks to explore a vector from a core scaffold to form additional interactions with the target protein. A rational approach to this is to probe the region of a protein accessed by a vector with a systematic placement of pharmacophore features in 3D, particularly when bound structures are not available. Herein, we present bbSelect, an open-source tool built to map the placements of pharmacophore features in 3D Euclidean space from a library of R-groups, employing partitioning to drive a diverse and systematic selection to a user-defined size.

MELLODDY: Cross-pharma Federated Learning at Unprecedented Scale Unlocks Benefits in QSAR without Compromising Proprietary Information.

Federated multipartner machine learning has been touted as an appealing and efficient method to increase the effective training data volume and thereby the predictivity of models, particularly when the generation of training data is resource-intensive. In the landmark MELLODDY project, indeed, each of ten pharmaceutical companies realized aggregated improvements on its own classification or regression models through federated learning. To this end, they leveraged a novel implementation extending multitask learning across partners, on a platform audited for privacy and security.

Blinded Predictions and Post Hoc Analysis of the Second Solubility Challenge Data: Exploring Training Data and Feature Set Selection for Machine and Deep Learning Models.

Accurate methods to predict solubility from molecular structure are highly sought after in the chemical sciences. To assess the state of the art, the American Chemical Society organized a "Second Solubility Challenge" in 2019, in which competitors were invited to submit blinded predictions of the solubilities of 132 drug-like molecules. In the first part of this article, we describe the development of two models that were submitted to the Blind Challenge in 2019 but which have not previously been reported.

Alchemical Free Energy Methods Applied to Complexes of the First Bromodomain of BRD4.

Accurate and rapid predictions of the binding affinity of a compound to a target are one of the ultimate goals of computer aided drug design. Alchemical approaches to free energy estimations follow the path from an initial state of the system to the final state through alchemical changes of the energy function during a molecular dynamics simulation. Herein, we explore the accuracy and efficiency of two such techniques: relative free energy perturbation (FEP) and multisite lambda dynamics (MSλD).

Is There an Optimal Antiplatelet Strategy after Gastrointestinal Bleeding in Patients with Coronary Artery Disease?

Gastrointestinal bleeding after percutaneous coronary intervention (PCI) is a not too uncommon clinical situation and is associated with high morbidity and mortality. After initial treatment, a number of clinical decisions must be made weighing the risks of ischemic events and future bleeding. In particular, healthcare providers must carefully balance the effectiveness of antiplatelet therapy in the secondary prevention of coronary events, primarily future spontaneous myocardial infarction and stent thrombosis, against the risk of major, most commonly gastrointestinal bleeding.

Structural variation of protein-ligand complexes of the first bromodomain of BRD4.

The bromodomain-containing protein 4 (BRD4), a member of the bromodomain and extra-terminal domain (BET) family, plays a key role in several diseases, especially cancers. With increased interest in BRD4 as a therapeutic target, many X-ray crystal structures of the protein in complex with small molecule inhibitors are publicly available over the recent decade. In this study, we use this structural information to investigate the conformations of the first bromodomain (BD1) of BRD4.

A Turing Test for Molecular Generators.

Machine learning approaches promise to accelerate and improve success rates in medicinal chemistry programs by more effectively leveraging available data to guide a molecular design. A key step of an automated computational design algorithm is molecule generation, where the machine is required to design high-quality, drug-like molecules within the appropriate chemical space. Many algorithms have been proposed for molecular generation; however, a challenge is how to assess the validity of the resulting molecules.

Shift Work Sleep Disorder and Night Shift Work Significantly Impair Erectile Function.

Objectives: Here we examine the association between shift work sleep disorder (SWSD) and erectile dysfunction (ED) in shift workers.

Methods: Men presenting to a single andrology clinic between January 2014 and July 2017 completed validated questionnaires: International Index of Erectile Function (IIEF), Patient Health Questionnaire-9 (PHQ-9), and the nonvalidated SWSD Questionnaire. Men were also asked about shift work schedule, comorbidities, phosphodiesterase 5 (PDE5) inhibitor use, and testosterone use.

Guidelines for Recurrent Neural Network Transfer Learning-Based Molecular Generation of Focused Libraries.

Deep learning approaches have become popular in recent years in the field of molecular design. While a variety of different methods are available, it is still a challenge to assess and compare their performance. A particularly promising approach for automated drug design is to use recurrent neural networks (RNNs) as SMILES generators and train them with the learning procedure called "transfer learning".

Increased Risk of Hypogonadal Symptoms in Shift Workers With Shift Work Sleep Disorder.

Objective: To examine the association between shift work sleep disorder (SWSD), a primary circadian rhythm disorder characterized by excessive day-time sleepiness associated with shift work, and hypogonadal symptoms in shift workers.

Methods: Men presenting to an andrology clinic between July 2014 and June 2017 completed questionnaires assessing shift work schedule, SWSD risk, and hypogonadal symptoms ([quantitative] Androgen Deficiency in the Aging Male [qADAM, ADAM]). The impact of nonstandard shift work and SWSD on responses to qADAM and ADAM was assessed using ANOVA and linear regression.

Correction to: BRADSHAW: a system for automated molecular design.

The original version of this article unfortunately contained some mistakes in the references.

BRADSHAW: a system for automated molecular design.

This paper introduces BRADSHAW (Biological Response Analysis and Design System using an Heterogenous, Automated Workflow), a system for automated molecular design which integrates methods for chemical structure generation, experimental design, active learning and cheminformatics tools. The simple user interface is designed to facilitate access to large scale automated design whilst minimising software development required to introduce new algorithms, a critical requirement in what is a very fast moving field. The system embodies a philosophy of automation, best practice, experimental design and the use of both traditional cheminformatics and modern machine learning algorithms.

Shift Workers With Shift Work Disorder Have Worse Lower Urinary Tract Symptoms.

Objective: To examine the association between shift work or shift work disorder (SWD) and lower urinary tract symptoms (LUTS). Nonstandard shift workers are defined as those working shifts outside of a normal 7 AM-6 PM work day.

Methods: Men presenting to a single andrology clinic between July 2014 and June 2017 completed questionnaires that included questions about work schedules, shift work status, SWD[1][1], personal well-being via the Patient Health Questionnaire-9, and LUTS (International Prostate Symptom Score [IPSS]).

De Novo Molecule Design by Translating from Reduced Graphs to SMILES.

A key component of automated molecular design is the generation of compound ideas for subsequent filtering and assessment. Recently deep learning approaches have been explored as alternatives to traditional de novo molecular design techniques. Deep learning algorithms rely on learning from large pools of molecules represented as molecular graphs (generally SMILES), and several approaches can be used to tailor the generated molecules to defined regions of chemical space.

Nuisance Compounds, PAINS Filters, and Dark Chemical Matter in the GSK HTS Collection.

High-throughput screening (HTS) hits include compounds with undesirable properties. Many filters have been described to identify such hits. Notably, pan-assay interference compounds (PAINS) has been adopted by the community as the standard term to refer to such filters, and very useful guidelines have been adopted by the American Chemical Society (ACS) and subsequently triggered a healthy scientific debate about the pitfalls of draconian use of filters.

Advances in the management of dyslipidemia.

Purpose Of Review: Cardiovascular disease is the leading cause of morbidity and mortality in the United States and therapies aimed at lipid modification are important for the reduction of cardiovascular risk. There have been many exciting advances in lipid management over the recent years. This review discusses these recent advances as well as the direction of future studies.

Age and duration of testosterone therapy predict time to return of sperm count after human chorionic gonadotropin therapy.

Objective: To determine factors that influence sperm recovery after T-associated infertility.

Design: Clinical retrospective study.

Setting: Academic male-infertility urology clinic.

Gain in Insurance Coverage and Residual Uninsurance Under the Affordable Care Act: Texas, 2013-2016.

Objectives: To examine the effects of the Affordable Care Act's (ACA's) Marketplace on Texas residents and determine which population subgroups benefited the most and which the least.

Methods: We analyzed insurance coverage rates among nonelderly Texas adults using the Health Reform Monitoring Survey-Texas from September 2013, just before the first open enrollment period in the Marketplace, through March 2016.

Results: Texas has experienced a roughly 6-percentage-point increase in insurance coverage (from 74.

Design Principles for Fragment Libraries: Maximizing the Value of Learnings from Pharma Fragment-Based Drug Discovery (FBDD) Programs for Use in Academia.

Fragment-based drug discovery (FBDD) is well suited for discovering both drug leads and chemical probes of protein function; it can cover broad swaths of chemical space and allows the use of creative chemistry. FBDD is widely implemented for lead discovery in industry but is sometimes used less systematically in academia. Design principles and implementation approaches for fragment libraries are continually evolving, and the lack of up-to-date guidance may prevent more effective application of FBDD in academia.

Structurally Diverse Mitochondrial Branched Chain Aminotransferase (BCATm) Leads with Varying Binding Modes Identified by Fragment Screening.

Inhibitors of mitochondrial branched chain aminotransferase (BCATm), identified using fragment screening, are described. This was carried out using a combination of STD-NMR, thermal melt (Tm), and biochemical assays to identify compounds that bound to BCATm, which were subsequently progressed to X-ray crystallography, where a number of exemplars showed significant diversity in their binding modes. The hits identified were supplemented by searching and screening of additional analogues, which enabled the gathering of further X-ray data where the original hits had not produced liganded structures.

An analysis of the attrition of drug candidates from four major pharmaceutical companies.

The pharmaceutical industry remains under huge pressure to address the high attrition rates in drug development. Attempts to reduce the number of efficacy- and safety-related failures by analysing possible links to the physicochemical properties of small-molecule drug candidates have been inconclusive because of the limited size of data sets from individual companies. Here, we describe the compilation and analysis of combined data on the attrition of drug candidates from AstraZeneca, Eli Lilly and Company, GlaxoSmithKline and Pfizer.

The Discovery of in Vivo Active Mitochondrial Branched-Chain Aminotransferase (BCATm) Inhibitors by Hybridizing Fragment and HTS Hits.

The hybridization of hits, identified by complementary fragment and high throughput screens, enabled the discovery of the first series of potent inhibitors of mitochondrial branched-chain aminotransferase (BCATm) based on a 2-benzylamino-pyrazolo[1,5-a]pyrimidinone-3-carbonitrile template. Structure-guided growth enabled rapid optimization of potency with maintenance of ligand efficiency, while the focus on physicochemical properties delivered compounds with excellent pharmacokinetic exposure that enabled a proof of concept experiment in mice. Oral administration of 2-((4-chloro-2,6-difluorobenzyl)amino)-7-oxo-5-propyl-4,7-dihydropyrazolo[1,5-a]pyrimidine-3-carbonitrile 61 significantly raised the circulating levels of the branched-chain amino acids leucine, isoleucine, and valine in this acute study.

QSAR workbench: automating QSAR modeling to drive compound design.

We describe the QSAR Workbench, a system for the building and analysis of QSAR models. The system is built around the Pipeline Pilot workflow tool and provides access to a variety of model building algorithms for both continuous and categorical data. Traditionally models are built on a one by one basis and fully exploring the model space of algorithms and descriptor subsets is a time consuming basis.