QPowered Compound2DeNovoDrugPropMax –A Novel Programmatic Tool Incorporating Deep Learning and In Silico Methods for Automated In Silico Bio- Activity Discovery for any Compound of Interest
Abstract
Network data is composed of nodes and edges. Successful application of machine learning/deeplearning algorithms on network data to make node classification and link prediction have been shownin the area of social networks through which highly customized suggestions are offered to socialnetwork users. Similarly one can attempt the use of machine learning/deep learning algorithms onbiological network data to generate predictions of scientific usefulness. In the presented work,compound-drug target interaction network data set from bindingDB has been used to train deeplearning neural network and a multi class classification has been implemented to classify PubChemcompound queried by the user into class labels of PBD IDs. This way target interaction prediction forPubChem compounds is carried out using deep learning. The user is required to input the PubChemCompound ID (CID) of the compound the user wishes to gain information about its predictedbiological activity and the tool outputs the RCSB PDB IDs of the predicted drug target interaction forthe input CID. Further the tool also optimizes the compound of interest of the user toward druglikeness properties through a deep learning based structure optimization with a deep learning baseddrug likeness optimization protocol. The tool also incorporates a feature to perform automated InSilico modelling for the compounds and the predicted drug targets to uncover their protein-ligandinteraction profiles. The program is hosted, supported…
How strong is the evidence?
Evidence Hub reads this as a simulation / modelling, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
Read the paper
Related studies
- Quantum dots: synthesis, bioapplications, and toxicity
- A Guide to In Silico Drug Design
- In silico toxicology protocols
- Automated in Silico Design of Homogeneous Catalysts
- In-silico strategies in nano-drug design: Bridging nanomaterials and pharmacological applications
- mRNA codon optimization with quantum computers
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.