Biology subjects

Samanta, S.

Publications and source records attributed to Samanta, S..

3 recordsLinked to original sources

bioRxiv2020

Naphthalene monoimide derivative ameliorates amyloid burden and cognitive decline in a transgenic mouse model of Alzheimer's disease

Alzheimers disease (AD) is a major neurodegenerative disorder and the leading cause of dementia worldwide. Predominantly, misfolding and aggregation of amyloid-{beta} (A{beta}) peptides associated with multifaceted toxicity is the neuropathological hallmark of AD pathogenesis and thus, primary therapeutic target to ameliorate neuronal toxicity and cognitive deficits. Herein, we report the design, synthesis and evaluation of small molecule inhibitors with naphthalene monoimide scaffold to ameliorate in vitro and in vivo amyloid induced neurotoxicity. The detailed studies established TGR63 as the lead candidate to rescue neuronal cells from amyloid toxicity. The in silico studies showed disruption of salt bridges and intermolecular hydrogen bonding interactions within A{beta}42 fibrils by the interaction of TGR63, causing destabilization of A{beta}42 assembly. Remarkably, TGR63 treatment showed a significant reduction in cortical and hippocampal amyloid burden in the progressive stages of APP/PS1 AD mice brain. Various behavioral tests demonstrated rescued cognitive deficits. The excellent biocompatibility, BBB permeability and therapeutic efficacy to reduce amyloid burden make TGR63 a promising candidate for the treatment of AD.

neuroscience↗

bioRxiv2020

VAE-Sim: a novel molecular similarity measure based on a variational autoencoder

Molecular similarity is an elusive but core unsupervised cheminformatics concept, yet different fingerprint encodings of molecular structures return very different similarity values even when using the same similarity metric. Each encoding may be of value when applied to other problems with objective or target functions, implying that a priori none is better than the others, nor than encoding-free metrics such as maximum common substructure (MCSS). We here introduce a novel approach to molecular similarity, in the form of a variational autoencoder (VAE). This learns the joint distribution p(z|x) where z is a latent vector and x are the (same) input/output data. It takes the form of a bowtie-shaped artificial neural network. In the middle is a bottleneck layer or latent vector in which inputs are transformed into, and represented as, a vector of numbers (encoding), with a reverse process (decoding) seeking to return the SMILES string that was the input. We train a VAE on over 6 million druglike molecules and natural products (including over one million in the final holdout set). The VAE vector distances provide a rapid and novel metric for molecular similarity that is both easily and rapidly calculated. We describe the method and its application to a typical similarity problem in cheminformatics.

pharmacology and toxicology↗

bioRxiv2020

DeepGraphMol, a multi-objective, computational strategy for generating molecules with desirable properties: a graph convolution and reinforcement learning approach

We address the problem of generating novel molecules with desired interaction properties as a multi-objective optimization problem. Interaction binding models are learned from binding data using graph convolution networks (GCNs). Since the experimentally obtained property scores are recognised as having potentially gross errors, we adopted a robust loss for the model. Combinations of these terms, including drug likeness and synthetic accessibility, are then optimized using reinforcement learning based on a graph convolution policy approach. Some of the molecules generated, while legitimate chemically, can have excellent drug-likeness scores but appear unusual. We provide an example based on the binding potency of small molecules to dopamine transporters. We extend our method successfully to use a multi-objective reward function, in this case for generating novel molecules that bind with dopamine transporters but not with those for norepinephrine. Our method should be generally applicable to the generation in silico of molecules with desirable properties.

bioinformatics↗