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.

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.

Synthesis, Structure-Activity Relationship, and Antimalarial Efficacy of 6-Chloro-2-arylvinylquinolines.

There is an urgent need to develop new efficacious antimalarials to address the emerging drug-resistant clinical cases. Our previous phenotypic screening identified styrylquinoline as a promising antimalarial compound. To optimize , we herein report a detailed structure-activity relationship study of 2-arylvinylquinolines, leading to the discovery of potent, low nanomolar antiplasmodial compounds against a CQ-resistant Dd2 strain, with excellent selectivity profiles (resistance index < 1 and selectivity index > 200).

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.