# K-talysticFlow (KAST) Documentation :::{only} html ```{raw} html
K-talysticFlow (KAST)

K-atalystic Automated Screening Taskflow β€” Automated Deep Learning for Molecular Bioactivity Prediction

πŸ“– Overview  Β·  πŸš€ Installation  Β·  ⚑ Quick Start  Β·  πŸ™ GitHub

``` ::: *** ## What is KAST? K-talysticFlow (KAST) is an open-source pipeline that democratizes the use of deep learning for molecular bioactivity prediction in drug discovery and virtual screening workflows. KAST was developed at the Laboratory of Molecular Modeling (LMM-UEFS) to provide researchers with a reproducible, end-to-end solution β€” from data preparation to prediction β€” without requiring deep expertise in machine learning infrastructure. The pipeline is built on [DeepChem](https://github.com/deepchem/deepchem) and TensorFlow, using Morgan/ECFP fingerprints as molecular descriptors and a MultitaskClassifier neural network trained from scratch on user-provided bioactivity data. What can you use KAST for? Here are some examples: - Predict the bioactivity of small drug-like molecules against a biological target - Rank large compound libraries by predicted probability of activity - Train a custom deep learning model using your own active/inactive dataset - Evaluate model quality with ROC-AUC, enrichment factor, and cross-validation - Export ranked candidate lists for downstream experimental validation KAST is a machine learning training and inference tool β€” it learns from your data and builds a target-specific model. It does not ship with pre-trained models for arbitrary targets. *** ## Quick Start The fastest way to get started is to set up the Conda environment and launch the interactive menu: ```bash conda env create -f environment.yml conda activate ktalysticflow python main.py ``` Then follow the step-by-step pipeline: ``` [1] Prepare Data β†’ Clean and organize your SMILES dataset [2] Featurize β†’ Generate Morgan/ECFP fingerprints [3] Train Model β†’ Build your deep learning model from scratch [4] Evaluate β†’ ROC-AUC, cross-validation, enrichment factor [5] Predict β†’ Screen new molecules and export ranked results ``` *** ## About KAST is developed and maintained at the [Laboratory of Molecular Modeling (LMM-UEFS)](https://lmm.uefs.br/) by KΓ©ssia Souza Santos. Contributions, issues, and suggestions are welcome via the [GitHub repository](https://github.com/kelsouzs/KAST). **Funding:** This project was developed with support from [CNPq](https://www.gov.br/cnpq/) (undergraduate research scholarship, PIBIC/IC) and is currently continued under a [CAPES](https://www.gov.br/capes/) graduate research scholarship (MSc). *** ```{toctree} :maxdepth: 1 :caption: Getting Started :hidden: getting-started/overview getting-started/installation getting-started/quick-start ``` ```{toctree} :maxdepth: 1 :caption: User Guide :hidden: user-guide/how-it-works user-guide/step-by-step user-guide/data-preparation user-guide/parallel-processing user-guide/outputs ``` ```{toctree} :maxdepth: 1 :caption: Support :hidden: support/faq support/troubleshooting support/configuration ```