Due to the close relationship of diabetes with hypertension reported in various research, a set of pyridine derivatives with US FDA-approved drug cores were designed and integrated by artificial intelligence. Novel pyridines were designed and synth...
In the recent COVID-19 pandemic, SARS-CoV-2 infection spread worldwide. The 3C-like protease (3CLpro) is a promising drug target for SARS-CoV-2. We constructed a deep learning-based convolutional neural network-quantitative structure-activity relat...
In the early stages of drug discovery, various experimental and computational methods are used to measure the specificity of small molecules against a target protein. The selectivity of small molecules remains a challenge leading to off-target side ...
Marburg virus (MARV) is a sporadic outbreak of a zoonotic disease that causes lethal hemorrhagic fever in humans. We propose a deep learning model with resampling techniques and predict the inhibitory activity of MARV from unknown compounds in the v...
Since December 2019, SARS-CoV-2 has continued to spread rapidly around the world. The effective drugs may provide a long-term strategy to combat this virus. The main protease (Mpro) and papain-like protease (PLpro) are two important targets for the ...
Predicting novel small molecule bioactivities for the target deconvolution, hit-to-lead optimization in drug discovery research, requires molecular representation. Previous reports have demonstrated that machine learning (ML) and deep learning (DL) h...
Conventional experimental approaches used for the evaluation of the proarrhythmic potential of compounds in the drug discovery process are expensive and time consuming but an integral element in the safety profile required for a new drug to be appro...
We applied genetic programming approaches to understand the impact of descriptors on inhibitory effects of serine protease inhibitors of () and the discovery of new inhibitors as drug candidates. The experimental dataset of serine protease inhibit...