Novartis Open Source Initiative

Exploratory Graphics for PKPD data

Implementation of caMPO (Correlation-aware Multiparameter Optimization), a method for deriving data-driven non-negative MPO weights from property correlation structure in drug discovery.

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A Python library for benchmarking uncertainty estimation and quantification methods for Machine Learning models predictions.

This pipeline is designed for AAV integration site analysis using Target Enrichment Sequencing (TES) coupled with PacBio long-read sequencing. It enables the detection of integration site breakpoints and provides information on the structure of the integrated AAV vector genome to support the safety assessment of AAV-based gene therapies.

Bayesian logistic regression with optional EX/NEX modeling enables flexible borrowing from historical or concurrent data. The safety model supports dose-escalation in adaptive Phase I oncology trials with multiple drugs. See Neuenschwander et al. (2008, 2016) for methodology.

Data pre-processing and chemprop model for frequent hitter prediction

Tool-set to support Bayesian evidence synthesis in R

R package for supporting exploratory graphics at http://opensource.nibr.com/xgx