The problem of conformational effects

Automatic Prediction of Peak Optical Absorption Wavelengths in Molecules Using Convolutional Neural Networks

Molecular design depends heavily on optical properties for applications such as solar cells and polymer-based batteries. Accurate prediction of these properties is essential, and multiple predictive methods exist, from ab initio to data-driven techniques. Although theoretical methods, such as time-dependent density functional theory (TD-DFT) calculations, have well-established physical relevance and are among the most popular methods in computational physics and chemistry, they exhibit errors that are inherent in their approximate nature. These high-throughput electronic structure calculations also incur a substantial computational cost. With the emergence of big-data initiatives, cost-effective, data-driven methods have gained traction, although their usability is highly contingent on the degree of data quality and sparsity. In this study, we present a workflow that employs deep residual convolutional neural networks (DR-CNN) and gradient boosting feature selection to predict peak optical absorption wavelengths (λmax) exclusively from SMILES representations of dye molecules and solvents; one would normally measure λmax using UV-vis absorption spectroscopy. We use a multifidelity modeling approach, integrating 34,893 DFT calculations and 26,395 experimentally derived λmax data, to deliver more accurate predictions via a Bayesian-optimized gradient boosting machine. Our approach is benchmarked against the state of the art that is reported in the scientific literature; results demonstrate that learnt representations via a DR-CNN workflow that is integrated with other machine learning methods can accelerate the design of molecules for specific optical characteristics.

Different routes for the construction of biologically active diversely functionalized bicyclo[3.3.1]nonanes: an exploration of new perspectives for anticancer chemotherapeutics

Cancer is the second most high-morbidity disease throughout the world. From ancient days, natural products have been known to possess several biological activities, and research on natural products is one of the most enticing areas where scientists are engrossed in the extraction of valuable compounds from various plants to isolate many life-saving medicines, along with their other applications. It has been noticed that the bicyclo[3.3.1]nonane moiety is predominant in most biologically active natural products owing to its exceptional characteristics compared to others. Many derivatives of bicyclo[3.3.1]nonane are attractive to researchers for use in asymmetric catalysis or as potent anticancer entities along with their successful applications as ion receptors, metallocycles, and molecular tweezers. Therefore, this review article discusses several miscellaneous synthetic routes for the construction of bicyclo[3.3.1]nonanes and their heteroanalogues in association with the delineation of their anticancer activities with few selective compounds.