S.No.	Software Name	Web Address	Publicly Available	Architecture Details/Flowchart	Publication for Developed Database	Min System Requirement	Other Details
1	miRBiom	https://scbb.ihbt.res.in/miRbiom-webserver/	Yes	http://14.139.62.220/software_uploads/581.png	http://14.139.62.220/published_databases/582.pdf	Mac OS, Windows, Linux, 4GB RAM, python3	A novel algorithms to accurately predict miRNA profiles in the absence of profiling experiments
2	RBPSpot	https://scbb.ihbt.res.in/RBPSpot/	Yes	http://14.139.62.220/software_uploads/584.jpg	http://14.139.62.220/published_databases/583.pdf	Mac OS, Windows, Linux, 4GB RAM, python3	RBPSpot can identify RBP binding sites in the human system and can also be used to develop new models, making it a valuable resource in the area of regulatory system studies.
3	DeepPlnc	https://scbb.ihbt.res.in/DeepPlnc/	Yes	http://14.139.62.220/software_uploads/585.jpg	http://14.139.62.220/published_databases/586.pdf	Mac OS, Windows, Linux, 4GB RAM, python3	A bi-modal CNN based deep-learning system, DeepPlnc, to identify plant lncRNAs with high accuracy while using sequence and structural properties.
4	miWords	https://scbb.ihbt.res.in/miWords/index.php	Yes	http://14.139.62.220/software_uploads/588.png	http://14.139.62.220/published_databases/587.pdf	Mac OS, Windows, Linux, 4GB RAM, python3	miWords, a composite deep learning system of transformers and convolutional neural networks which sees genome as a pool of sentences made of words with specific occurrence preferences and contexts, to accurately identify pre-miRNA regions across plant genomes.
5	RicePathDLNet	https://fgcsl.ihbt.res.in/RicePathDLNet/	Yes	http://14.139.62.220/software_uploads/590.png	http://14.139.62.220/published_databases/591.pdf	Mac OS, Windows, Linux, 4GB RAM, python3	A deep learning-based rice network model that has explored the quantitative differences resulting in the distinct rice network architecture.
6	A-HIOT	https://gitlab.com/neeraj-24/A-HIOT	Yes	http://14.139.62.220/software_uploads/593.png	http://14.139.62.220/published_databases/592.pdf	Mac OS, Windows, Linux, 4GB RAM, python3	The A-HIOT is a reliable approach for bridging the long-standing gap between ligand-based and structure-based VS and finding optimized hits for the desired or fixed receptor.