AI for Drug Suitability
Open-source tools, demos, and software built by our scientists and engineers — free to explore and use.
BigQUIC: Big Quadratic Inverse Covariance Estimation
Use Newton’s method, coordinate descent, and METIS clustering to solve the L1 regularized Gaussian MLE inverse covariance matrix estimation problem. https://cran.r-project.org/web/packages/BigQuic/index.html
COUSCOus
Motivation: Current methods for predicting protein residue contacts are valuable but incomplete and do not fully agree. We developed a new method, COUSCOus, that combines advanced statistical techniques to improve accuracy. Our method consistently outperforms the established PSICOV tool across multiple benchmarks and independent tests. This demonstrates that superior statistical approaches can significantly advance protein contact prediction and related fields like gene network analysis.
DeepCrystal
QCRI deep learning models for crystallization propensity prediction, DeepCrystal and BCrystal is ready to compute.
Deepsol
Protein solubility plays a vital role in pharmaceutical research and production yield. For a given protein, the extent of its solubility can represent the quality of its function, and is ultimately defined by its sequence. Thus, it is imperative to develop novel, highly accurate in silico sequence-based protein solubility predictors.
Deployable Code for Early Prediabetes Detection: The PRISQ Model
Leveraging data from the Qatar Biobank, we have created and validated a deployable algorithm for prediabetes screening. The PRISQ model’s code takes basic health metrics as input and outputs a clear risk category (Low, Moderate, High). This allows for seamless integration into digital health platforms, electronic health records, and public screening tools, providing a cost-effective, first-line defense against type 2 diabetes for Middle Eastern populations.
OutSingle
A Python tool for finding outliers in RNA-Seq gene expression count data using SVD/OHT