Blood‐brain‐barrier permeability (BBBP) is an important property that is used to establish the drug‐likeness of a molecule, as it establishes whether the molecule can cross the BBB when desired. It also eliminates those molecules which are not supposed to cross the barrier, as doing so would lead to toxicity. BBBP can be measured in vivo, in vitro or in silico. With the advent and subsequent rise of in silico methods for virtual drug screening, quite a bit of work has been done to predict this feature using statistical machine learning (ML) and deep learning (DL) based methods. In this work a mixed DL‐based model, consisting of a Multi‐layer Perceptron (MLP) and Convolutional Neural Network layers, has been paired with Mol2vec. Mol2vec is a convenient and unsupervised machine learning technique which produces high‐dimensional vector representations of molecules and its molecular substructures. These succinct vector representations are utilized as inputs to the mixed DL model that is used for BBBP predictions. Several well‐known benchmarks incorporating BBBP data have been used for supervised training and prediction by our mixed DL model which demonstrates superior results when compared to existing ML and DL techniques used for predicting BBBP.
Transition metal complexes with different ligand systems have been developed. Majority of them are derived from heteroaromatic cycles and Schiff bases of variety of aromatic aldehydes, diketones with amino acids, polyamines, N-aromatic cycles and ethylenediamines. Metal complexes of Schiff bases are used as polymers, dyes and in various biological systems. They show antimicrobial activity and can be used as suitable drug to treat bacterial and fungal infections. Most of the Cu, Ni and Co complexes are also capable of binding and cleaving DNA.
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