# Concepts and Applications of Conformal Prediction in Computational Drug Discovery

arXiv: https://arxiv.org/abs/1908.03569 · HTML: https://mlsanity.com/paper/1908.03569/

## Key facts

- Cited by 13 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 9 Aug 2019.

## Built on by (most cited first, 13 of 13)

- [Selection by Prediction with Conformal p-values](https://mlsanity.com/paper/2210.01408/) (2022)
- [Conformal Prediction with Temporal Quantile Adjustments](https://mlsanity.com/paper/2205.09940/) (2022)
- [Conformal Prediction via Regression-as-Classification](https://mlsanity.com/paper/2404.08168/) (2024)
- [Assigning Confidence to Molecular Property Prediction](https://mlsanity.com/paper/2102.11439/) (2021)
- [Conformal Drug Property Prediction with Density Estimation under Covariate Shift](https://mlsanity.com/paper/2310.12033/) (2023)
- [Semiparametric conformal prediction](https://mlsanity.com/paper/2411.02114/) (2024)
- [Conformal Prediction Intervals with Temporal Dependence](https://mlsanity.com/paper/2205.12940/) (2022)
- [Exact and Approximate Conformal Inference for Multi-Output Regression](https://mlsanity.com/paper/2210.17405/) (2022)
- [TwinBooster: Synergising Large Language Models with Barlow Twins and Gradient Boosting for Enhanced Molecular Property Prediction](https://mlsanity.com/paper/2401.04478/) (2024)
- [An Empirical Study of Conformal Prediction in LLM with ASP Scaffolds for Robust Reasoning](https://mlsanity.com/paper/2503.05439/) (2025)
- [Efficient Normalized Conformal Prediction and Uncertainty Quantification for Anti-Cancer Drug Sensitivity Prediction with Deep Regression Forests](https://mlsanity.com/paper/2402.14080/) (2024)
- [Development and Evaluation of Conformal Prediction Methods for QSAR](https://mlsanity.com/paper/2304.00970/) (2023)
- [ConfHit: Conformal Generative Design with Oracle Free Guarantees](https://mlsanity.com/paper/2603.07371/) (2026)

## Abstract (arXiv)

Estimating the reliability of individual predictions is key to increase the adoption of computational models and artificial intelligence in preclinical drug discovery, as well as to foster its application to guide decision making in clinical settings. Among the large number of algorithms developed over the last decades to compute prediction errors, Conformal Prediction (CP) has gained increasing attention in the computational drug discovery community. A major reason for its recent popularity is the ease of interpretation of the computed prediction errors in both classification and regression tasks. For instance, at a confidence level of 90% the true value will be within the predicted confidence intervals in at least 90% of the cases. This so called validity of conformal predictors is guaranteed by the robust mathematical foundation underlying CP. The versatility of CP relies on its minimal computational footprint, as it can be easily coupled to any machine learning algorithm at little computational cost. In this review, we summarize underlying concepts and practical applications of CP with a particular focus on virtual screening and activity modelling, and list open source implementations of relevant software. Finally, we describe the current limitations in the field, and provide a perspective on future opportunities for CP in preclinical and clinical drug discovery.