Access AI-ready drug discovery datasets and benchmarks for machine learning in therapeutics.
npx skills add https://github.com/Sologa/codex-pipeline --skill pytdc-sologa
Please help me install this Agent Skill. Skill: pytdc Source: https://github.com/Sologa/codex-pipeline/tree/main/.codex/skills/pytdc Command: npx skills add https://github.com/Sologa/codex-pipeline --skill pytdc-sologa
This Skill provides access to curated datasets and benchmarks for drug discovery and development, streamlining machine learning model development for therapeutic applications.
Use the pytdc skill to load the 'Caco2_Wang' dataset for ADME prediction.
High-intent search queries and answers about installing and using this skill.
AI-ready drug discovery datasets provide curated molecular property prediction, drug-target interaction, and molecular generation benchmarks. They streamline therapeutic machine learning model development by supplying standardized data splits and evaluation metrics for consistent testing.
Standardized benchmarks for molecular property prediction include ADME and toxicity datasets. They provide consistent evaluation metrics and standardized data splits, allowing researchers to train models and evaluate drug toxicity or absorption performance accurately.
To load a dataset for drug-target interaction analysis, you access curated therapeutic machine learning benchmarks. These provide standardized data splits and chemical data processing utilities to facilitate training and evaluating interaction prediction models.
Yes, these datasets support de novo molecular generation tasks. They provide curated chemical data processing utilities and standardized benchmarks to evaluate generative models within therapeutic machine learning workflows.
No, you do not need external libraries for initial processing because these benchmarks include built-in chemical data processing utilities. They provide standardized data splits and evaluation metrics directly for therapeutic machine learning tasks.
Drug toxicity prediction models are evaluated using standardized metrics provided with the dataset. These benchmarks ensure consistent model evaluation by supplying predefined data splits and processing utilities for therapeutic machine learning.
galeepConvert SMILES strings and RDKit molecules into numerical features for machine learning.
LogauaEngstromLoad drug discovery datasets and run standardized benchmarks with scaffold splits.
Imad-OuteAnalyze and modify molecular structures in cheminformatics workflows.
Yezez9Perform molecular property prediction and featurization using DeepChem.
