SieveAI: Development of an Automated extensible and customisable drug discovery pipeline and its validation
Sahu, V. K.; Sand, A.; Ballav, S.; Raman, V.; Nagar, S.; Ranjan, A.; Basu, S.
Show abstract
Systematic Interaction Evaluation and Virtual Enhancement Analysis Interface (SieveAI) is an automated drug discovery pipeline developed to enhance the efficiency of virtual screening and computer-aided drug discovery processes. The molecular docking workflow encompasses acquiring, modeling, and pre-processing of molecular structure files, conducting docking with various algorithms, and subsequent analysis and interpretation of the outcomes by visualising or tabulating the results. While several open-source software tools are available to assist these operations at different steps of molecular docking, they often necessitate manual user intervention at every stage. To streamline and automate this extensive manual process and develop a comprehensive solution, we have developed an innovative, fully extensible, molecular docking pipeline SieveAI ((C)L-129927/2023). The same has been demonstrated in this manuscript. SieveAI works with a range of open-source libraries, packages, and programs to facilitate automated drug discovery using established programs and software. The package is accessible at https://miRNA.in/SieveAI.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Antivirals for Monkeypox Virus: Proposing an Effective Machine/Deep Learning Framework 96%
- Deep learning based predictive modeling to screen natural compounds against TNF-alpha for the potential management of Rheumatoid Arthritis: Virtual screening to comprehensive in silico investigation 96%
- Identification of Natural Antiviral Drug Candidates Against Tilapia Lake Virus: Computational Drug Design Approaches 96%
Similar papers in this journal
- A program to automate the discovery of drugs for West Nile and Dengue virus -- programmatic screening of over a billion compounds on PubChem, generation of drug leads and automated In Silico modelling 98%
- Target2DeNovoDrugPropMax : a novel programmatic tool incorporating deep learning and in silico methods for automated de novo drug design for any target of interest 98%
- Pharmacophore modeling, 2D-QSAR, Molecular Docking and ADME studies for the discovery of inhibitors of PBP2a in MRSA 96%
Similar papers in this journal
- RAFTS3G - An efficient and versatile clustering software to analyses in large protein datasets 95%
- PDAUG - a Galaxy based toolset for peptide library analysis, visualization, and machine learning modeling 95%
- Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions 95%
Similar papers in this journal
- DenovoProfiling: a webserver for de novo generated molecule library profiling 96%
- Computationally Grafting an IgE Epitope onto a Scaffold: Implications for a Pan Anti-Allergy Vaccine Design 94%
- Analysis of Mutations in Precision Oncology using The Automated, Accurate, and User-Friendly Web Tool PredictONCO 94%
Similar papers in this journal
- DSSP in Gromacs: tool for defining secondary structures of proteins in trajectories 95%
- DUBS: A Framework for Developing Directory of Useful Benchmarking Sets for Virtual Screening 95%
- Identification of Family-Specific Features in Cas9 and Cas12 Proteins: A Machine Learning Approach Using Complete Protein Feature Spectrum 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.