Hetero-polycyclic aromatic systems: A data-driven investigation of structure-property relationships.

Polycyclic aromatic systems (PASs) are pervasive compounds that have a substantial impact in chemistry and materials science. Although their specific structure-property relationships hold the key to the design of new functional molecules, a detailed understanding of these relationships remains elusive. To elucidate these relationships, we performed a data-driven investigation of the newly generated COMPAS-2 dataset, which contains ~500k molecules consisting of 11 types of aromatic and antiaromatic rings and ranging in size from one to ten rings.

COMPAS-2: a dataset of cata-condensed hetero-polycyclic aromatic systems.

Polycyclic aromatic systems are highly important to numerous applications, in particular to organic electronics and optoelectronics. High-throughput screening and generative models that can help to identify new molecules to advance these technologies require large amounts of high-quality data, which is expensive to generate. In this report, we present the largest freely available dataset of geometries and properties of cata-condensed poly(hetero)cyclic aromatic molecules calculated to date.

Guided diffusion for inverse molecular design.

The holy grail of materials science is de novo molecular design, meaning engineering molecules with desired characteristics. The introduction of generative deep learning has greatly advanced efforts in this direction, yet molecular discovery remains challenging and often inefficient. Herein we introduce GaUDI, a guided diffusion model for inverse molecular design that combines an equivariant graph neural net for property prediction and a generative diffusion model.

Stereo-electronic factors influencing the stability of hydroperoxyalkyl radicals: transferability of chemical trends across hydrocarbons and methods.

The hydroperoxyalkyl radicals (˙QOOH) are known to play a significant role in combustion and tropospheric processes, yet their direct spectroscopic detection remains challenging. In this study, we investigate molecular stereo-electronic effects influencing the kinetic and thermodynamic stability of a ˙QOOH along its formation path from the precursor, alkylperoxyl radical (ROO˙), and the depletion path resulting in the formation of cyclic ether + ˙OH. We focus on reactive intermediates encountered in the oxidation of acyclic hydrocarbon radicals: ethyl, isopropyl, isobutyl, -butyl, neopentyl, and their alicyclic counterparts: cyclohexyl, cyclohexenyl, and cyclohexadienyl.

Understanding the Role of Intramolecular Ion-Pair Interactions in Conformational Stability Using an Thermodynamic Cycle.

Intramolecular ion-pair interactions yield shape and functionality to many molecules. With proper orientation, these interactions overcome steric factors and are responsible for the compact structures of several peptides. In this study, we present a thermodynamic cycle based on isoelectronic and alchemical mutation to estimate the intramolecular ion-pair interaction energy.

Anomaly Detection Based on Time Series Data of Hydraulic Accumulator.

Although hydraulic accumulators play a vital role in the hydraulic system, they face the challenges of being broken by continuous abnormal pulsating pressure which occurs due to the malfunction of hydraulic systems. Hence, this study develops anomaly detection algorithms to detect abnormalities of pulsating pressure for hydraulic accumulators. A digital pressure sensor was installed in a hydraulic accumulator to acquire the pulsating pressure data.

Data-driven modeling of S → S excitation energy in the BODIPY chemical space: High-throughput computation, quantum machine learning, and inverse design.

Derivatives of BODIPY are popular fluorophores due to their synthetic feasibility, structural rigidity, high quantum yield, and tunable spectroscopic properties. While the characteristic absorption maximum of BODIPY is at 2.5 eV, combinations of functional groups and substitution sites can shift the peak position by ±1 eV.

Structure-based discovery of (S)-2-amino-6-(4-fluorobenzyl)-5,6,11,11a-tetrahydro-1H-imidazo[1',5':1,6]pyrido[3,4-b]indole-1,3(2H)-dione as low nanomolar, orally bioavailable autotaxin inhibitor.

Inhibition of extracellular secreted enzyme autotaxin (ATX) represents an attractive strategy for the development of new therapeutics to treat various diseases and a few inhibitors entered in clinical trials. We herein describe structure-based design, synthesis, and biological investigations revealing a potent and orally bioavailable ATX inhibitor 1. During the molecular docking and scoring studies within the ATX enzyme (PDB-ID: 4ZGA), the S-enantiomer (Gscore = -13.

Troubleshooting unstable molecules in chemical space.

A key challenge in automated chemical compound space explorations is ensuring veracity in minimum energy geometries-to preserve intended bonding connectivities. We discuss an iterative high-throughput workflow for connectivity preserving geometry optimizations exploiting the nearness between quantum mechanical models. The methodology is benchmarked on the QM9 dataset comprising DFT-level properties of 133 885 small molecules, wherein 3054 have questionable geometric stability.

Critical benchmarking of popular composite thermochemistry models and density functional approximations on a probabilistically pruned benchmark dataset of formation enthalpies.

First-principles calculation of the standard formation enthalpy, ΔH° (298 K), in such a large scale as required by chemical space explorations, is amenable only with density functional approximations (DFAs) and certain composite wave function theories (cWFTs). Unfortunately, the accuracies of popular range-separated hybrid, "rung-4" DFAs, and cWFTs that offer the best accuracy-vs-cost trade-off have until now been established only for datasets predominantly comprising small molecules; their transferability to larger systems remains vague. In this study, we present an extended benchmark dataset of ΔH° for structurally and electronically diverse molecules.

Findings from the 2017 HIV estimation round & trend analysis of key indicators 2010-2017: Evidence for prioritising HIV/AIDS programme in India.

Background & Objectives: The National AIDS Control Organisation (NACO) and the ICMR-National Institute of Medical Statistics, the nodal agency for conducting HIV estimations in India, have been generating HIV estimates regularly since 2003. The objective of this study was to describe India's biennial HIV estimation 2017 process, data inputs, tool, methodology and epidemiological assumptions used to generate the HIV estimates and trends of key indicators for 2010-2017 at national and State/Union Territory levels.

Methods: Demographic Projection (DemProj) and AIDS Impact Modules (AIM) of Spectrum 5.

A Supervised Machine Learning Approach to Detect the On/Off State in Parkinson's Disease Using Wearable Based Gait Signals.

Fluctuations in motor symptoms are mostly observed in Parkinson's disease (PD) patients. This characteristic is inevitable, and can affect the quality of life of the patients. However, it is difficult to collect precise data on the fluctuation characteristics using self-reported data from PD patients.

Detection of Parkinson's Disease from 3T T1 Weighted MRI Scans Using 3D Convolutional Neural Network.

Parkinson's Disease is a neurodegenerative disease that affects the aging population and is caused by a progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). With the onset of the disease, the patients suffer from mobility disorders such as tremors, bradykinesia, impairment of posture and balance, etc., and it progressively worsens in the due course of time.

Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson's Patients.

In the last few years, the importance of measuring gait characteristics has increased tenfold due to their direct relationship with various neurological diseases. As patients suffering from Parkinson's disease (PD) are more prone to a movement disorder, the quantification of gait characteristics helps in personalizing the treatment. The wearable sensors make the measurement process more convenient as well as feasible in a practical environment.

3D Textural, Morphological and Statistical Analysis of Voxel of Interests in 3T MRI Scans for the Detection of Parkinson's Disease Using Artificial Neural Networks.

Parkinson's disease is caused due to the progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc). Presently, with the exponential growth of the aging population across the world the number of people being affected by the disease is also increasing and it imposes a huge economic burden on the governments. However, to date, no therapy or treatment has been found that can completely eradicate the disease.

A Multichannel Convolutional Neural Network Architecture for the Detection of the State of Mind Using Physiological Signals from Wearable Devices.

Detection of the state of mind has increasingly grown into a much favored study in recent years. After the advent of smart wearables in the market, each individual now expects to be delivered with state-of-the-art reports about his body. The most dominant wearables in the market often focus on general metrics such as the number of steps, distance walked, heart rate, oximetry, sleep quality, and sleep stage.

The chemical space of B, N-substituted polycyclic aromatic hydrocarbons: Combinatorial enumeration and high-throughput first-principles modeling.

Combinatorial introduction of heteroatoms in the two-dimensional framework of aromatic hydrocarbons opens up possibilities to design compound libraries exhibiting desirable photovoltaic and photochemical properties. Exhaustive enumeration and first-principles characterization of this chemical space provide indispensable insights for rational compound design strategies. Here, for the smallest seventy-seven Kekulean-benzenoid polycyclic systems, we reveal combinatorial substitution of C atom pairs with the isosteric and isoelectronic B, N pairs to result in 7 453 041 547 842 (7.

Antinociceptive properties of shikonin: in vitro and in vivo studies.

Shikonin possess a diverse spectrum of pharmacological properties in multiple therapeutic areas. However, the nociceptive effect of shikonin is not largely known. To investigate the antinociceptive potential of shikonin, panel of GPCRs, ion channels, and enzymes involved in pain pathogenesis were studied.

Efficacy of a new model for delivering integrated TB and HIV services for people living with HIV/AIDS in Delhi -- case for a paradigm shift in national HIV/TB cross-referral strategy.

Under National TB/HIV framework, all TB patients are referred by Revised National Tuberculosis Programme (RNTCP) service providers to Integrated Counseling and Testing Centers (ICTCs) for voluntary counseling and testing (C&T) and ICTC "TB-suspects" are referred to RNTCP facilities for TB diagnosis and treatment. HIV-TB coinfected patients are then referred to Anti Retroviral Treatment (ART) center for initiation of ART between two weeks and two months of initiating TB treatment. During the third phase of National AIDS Control Programme (NACP-III, April 2007-April 2012), 30749/130503 (23.

Early diagnosis of HIV in children below 18 months using DNA PCR test--assessment of the effectiveness of PMTCT interventions and challenges in early initiation of ART in a resource-limited setting.

This study was conducted to assess efficacy of the current Indian Prevention of Mother-to-Child Transmission (PMTCT) protocol in 217 HIV-exposed infants, and to assess challenges in the early initiation of antiretroviral treatment (ART) in 18 (8.3%) infants with HIV, as determined by the HIV-1 DNA polymerase chain reaction (PCR) test at ≥ 6 weeks to <18 months of age. The mother-to-child transmission (MTCT) rate in 154 mother-baby pairs fully compliant with the PMTCT protocol was 5.