Functional microRNA-targeting drug discovery by graph-based deep learning.

MicroRNAs are recognized as key drivers in many cancers but targeting them with small molecules remains a challenge. We present RiboStrike, a deep-learning framework that identifies small molecules against specific microRNAs. To demonstrate its capabilities, we applied it to microRNA-21 (miR-21), a known driver of breast cancer.

ProtEC: A Transformer Based Deep Learning System for Accurate Annotation of Enzyme Commission Numbers.

The advancements in next-generation sequencing technologies have given rise to large-scale, open-source protein databases consisting of hundreds of millions of sequences. However, to make these sequences useful in biomedical applications, they need to be painstakingly annotated by curating them from literature. To counteract this problem, many automated annotation algorithms have been developed over the years including deep learning models, especially in recent times.

Functional microRNA-Targeting Drug Discovery by Graph-Based Deep Learning.

MicroRNAs are recognized as key drivers in many cancers, but targeting them with small molecules remains a challenge. We present RiboStrike, a deep learning framework that identifies small molecules against specific microRNAs. To demonstrate its capabilities, we applied it to microRNA-21 (miR-21), a known driver of breast cancer.

AMPDeep: hemolytic activity prediction of antimicrobial peptides using transfer learning.

Background: Deep learning's automatic feature extraction has proven to give superior performance in many sequence classification tasks. However, deep learning models generally require a massive amount of data to train, which in the case of Hemolytic Activity Prediction of Antimicrobial Peptides creates a challenge due to the small amount of available data.

Results: Three different datasets for hemolysis activity prediction of therapeutic and antimicrobial peptides are gathered and the AMPDeep pipeline is implemented for each.

MolData, a molecular benchmark for disease and target based machine learning.

Deep learning's automatic feature extraction has been a revolutionary addition to computational drug discovery, infusing both the capabilities of learning abstract features and discovering complex molecular patterns via learning from molecular data. Since biological and chemical knowledge are necessary for overcoming the challenges of data curation, balancing, training, and evaluation, it is important for databases to contain information regarding the exact target and disease of each bioassay. The existing depositories such as PubChem or ChEMBL offer the screening data for millions of molecules against a variety of cells and targets, however, their bioassays contain complex biological descriptions which can hinder their usage by the machine learning community.

Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development.

SARS-COV-2 has roused the scientific community with a call to action to combat the growing pandemic. At the time of this writing, there are as yet no novel antiviral agents or approved vaccines available for deployment as a frontline defense. Understanding the pathobiology of COVID-19 could aid scientists in their discovery of potent antivirals by elucidating unexplored viral pathways.

DeepMalaria: Artificial Intelligence Driven Discovery of Potent Antiplasmodials.

Antimalarial drugs are becoming less effective due to the emergence of drug resistance. Resistance has been reported for all available malaria drugs, including artemisinin, thus creating a perpetual need for alternative drug candidates. The traditional drug discovery approach of high throughput screening (HTS) of large compound libraries for identification of new drug leads is time-consuming and resource intensive.

Analysis and Simulation of Capacitor-Less ReRAM-Based Stochastic Neurons for the in-Memory Spiking Neural Network.

The stochastic neuron is a key for event-based probabilistic neural networks. We propose a stochastic neuron using a metal-oxide resistive random-access memory (ReRAM). The ReRAM's conducting filament with built-in stochasticity is used to mimic the neuron's membrane capacitor, which temporally integrates input spikes.