Skip to main content

A python lib for predicting small molecule-RNA interactions (SRIs)

Project description

Small molecules can bind RNAs to regulate their fate and functions, providing promising opportunities for treating human diseases. However, current tools for predicting small molecule-RNA interactions (SRIs) require prior knowledge of RNA tertiary structures, limiting their utility in drug discovery. Here, we present SMRTnet, a deep learning method to predict SRIs based on RNA secondary structure. By integrating large language models, convolutional neural networks, graph attention networks, and multimodal data fusion, SMRTnet achieves high performance across multiple experimental benchmarks, substantially outperforming existing state-of-the-art tools.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

smrtnet-0.11.tar.gz (6.4 MB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

smrtnet-0.11-py3-none-any.whl (7.5 MB view details)

Uploaded Python 3

File details

Details for the file smrtnet-0.11.tar.gz.

File metadata

  • Download URL: smrtnet-0.11.tar.gz
  • Upload date:
  • Size: 6.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.8.10

File hashes

Hashes for smrtnet-0.11.tar.gz
Algorithm Hash digest
SHA256 64049e5df59af0d444deddeb00bf328a23cc073d5cd9a68cd3df3d43ce75c25f
MD5 5daf5ca1757040bb91843e73901e228a
BLAKE2b-256 0fdf7d39b0a7e390df4daa79dce12e85fea396da35162508b72116fc82a0b608

See more details on using hashes here.

File details

Details for the file smrtnet-0.11-py3-none-any.whl.

File metadata

  • Download URL: smrtnet-0.11-py3-none-any.whl
  • Upload date:
  • Size: 7.5 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.8.10

File hashes

Hashes for smrtnet-0.11-py3-none-any.whl
Algorithm Hash digest
SHA256 58f29a0877e5a76c6af41edf3764fe43028166c45d96699e801862f5d1e0b07e
MD5 73a5583612f178bd858574ffccd3f69e
BLAKE2b-256 bf737f919079f9563f9670758332e8a29c423850499269e222ba896a215449f4

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page