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Drug-Target Interaction Prediction using ONNX models

Drutai is a deep learning–based framework for predicting interactions between small molecule drugs and protein targets. And this is a high-performance drug-target interaction prediction tool using ONNX models, with easier installation, higher performance as well as less dependency requirements . It supports multiple deep learning architectures including LSTMCNN, CNN, ConvMixer64, DSConv, MobileNetV2, ResNet18, and SEResNet.

Usage (single-table mode, recommended): drutai.predict -m lstmcnn
-i input.tsv
-t fasta_dir/
-o output.drutai

Usage (legacy mode, compatible with original drutai format): drutai.predict -m lstmcnn
--br relations.txt
--smile smiles.txt
-t fasta_dir/
-o output.drutai

Arguments: -m MODEL Model name (default: lstmcnn) -i INPUT Single-table TSV input (columns: sm, target, smile) --br FILE [Legacy] Drug-protein relation file (columns: sm, target) --smile FILE [Legacy] Drug SMILES file (columns: sm, smile) -t FASTA_DIR Directory containing per-target .fasta files -o OUTPUT Output file (default: predictions_onnx.drutai) -j THREADS Number of threads for feature extraction (default: all CPUs)

Available models: lstmcnn, cnn, convmixer64, dsconv, mobilenetv2, resnet_prea18_tf2, scaresnet


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