Table 9: List of commonly used terms in the paper
Term	Description
Activity (property)	Refers to the ability of a drug to bind to a specific target molecule and produce a biological effect. It is an important property to consider in drug discovery.
ADMET properties	Refers to the absorption, distribution, metabolism, excretion, and toxicity of a drug candidate. These properties play a critical role in determining the safety and efficacy of a drug.
Alpha	In conformal prediction, alpha refers to the user-defined confidence level used to construct the prediction sets. The parameter determines the amount of error that the user is willing to tolerate in the predictions and is typically set to a small value, such as 0.1 or 0.2. A smaller alpha typically results in a wider prediction set.
Calibration( of conformal prediction)	In the Mondrian Inductive Conformal Prediction framework, calibration refers to the procedure of using the calibration set to determine the threshold for each class. The objective is that the proportion of true labels across prediction sets matches the desired confidence level, as specified by the alpha parameter
Calibration set	A calibration set is a labeled subset of the dataset held out from the training set used to estimate the threshold for each class (in Mondrian ICP).
Conformal Prediction (CP)	A framework for constructing reliable prediction intervals or sets at a desired confidence level for a given machine learning model. The framework can be used with any machine learning model.
Coverage	Coverage of a conformal predictor is the proportion of times that the true label falls within the prediction sets produced by the predictor, over all the inputs.
Covariate shift	A phenomenon that occurs when the distribution of the input data changes between the training and testing phases of a machine learning model. It is assumed that the conditional distribution of the target variable given the input features (P(Y|X)) remains the same across the training and test sets.
Cumulative Distribution Function (CDF)	CDF gives the cumulative probability of the random variable taking on a value less than or equal to a particular value.
De novo Drug design model	A machine learning model used to generate novel drug candidates with desired properties. These models are based on generative models such as Variational Autoencoders, Reinforcement Learning, or Genetic algorithm and explore large chemical space.
Energy-based model	A type of model that learns a function that assigns low energy scores to data points that are similar to the training data and high energy scores to data points that are dissimilar.
Exchangeability	Exchangeability refers to the property of a sequence of random variables such that the joint distribution of any permutation of the variables is the same as the joint distribution of the original sequence. Independent and identically distributed (IID) implies exchangeability. Exchangeability is an important consideration in the Conformal prediction framework.
Fingerprint splitting	A method used to divide a dataset of molecules into training and testing sets based on the similarity of their molecular fingerprints.
Generative model	A type of machine learning model that learns the distribution of a dataset and can be used to generate new data points (in this case drug molecules) with similar properties.
Kernel Density Estimation	Kernel density estimation (KDE) is a non-parametric method for estimating the probability density function of a random variable based on a set of observations. It involves placing a kernel at each data point and summing the kernels to obtain a smoothed estimate of the density function.
Mondrian ICP	Mondrian Inductive Conformal Prediction (Mondrian ICP) is a variant of the conformal prediction framework that provides class-wise coverage guarantees for multi-class classification problems. The prediction sets are constructed to provide class-wise coverage guarantees, meaning that they are guaranteed to contain the true class label with a certain probability (determined by a user-defined confidence level) for each class.
Prediction set	A prediction set is a set of candidate labels (class values) for a given input. A prediction set is considered valid if it contains the true class label of an input.
Quantile Function	A function that maps a probability to a corresponding value in a distribution. It is the inverse of the cumulative distribution function.
Scaffold splitting	A method used to divide a dataset of molecules into training and testing sets while ensuring that the two sets have similar scaffold diversity.
Toxicity	Refers to the potential of a drug to cause harm to living organisms. It is an important ADMET property to consider in drug discovery.
Validity	A conformal predictor is said to be valid if its coverage level is equal to the user-defined significance level (usually denoted by alpha) used to construct the prediction sets.