## 1 Introduction  
The circadian rhythm regulates biological processes in 24 h cycles, and its alteration is associated with sleep disorders and can be related to pathologies such as cancer [1]. The circadian clock plays a fundamental role in the regulation of sleep, and its stability is increasingly recognized as a determining factor in various biological processes, which are essential for maintaining good health. To this end, photopharmacological manipulation of a core clock protein, mammalian Cryptochrome 1 (CRY1), is an effective strategy for the regulation of the circadian clock. CRY1 is a protein that influences the circadian period. Supported by the analysis of data and advanced computational modelling techniques, it has been established as an essential tool in the detection and characterization of compounds with an impact on circadian dynamics [2]. Recent studies have applied high-throughput screening and data analysis to identify potential drugs, taking advantage of advanced modelling and machine learning techniques [3].  
Structural approaches in drug discovery optimize efficiency and reduce costs, while the incorporation of screening methods allows the elimination of inappropriate molecules, such as toxic or inactive ones. After identifying 171 molecules that target functional domainsof the CRY1 protein, using structure-based drug design methods, and experimentally determining that 115 of these molecules were nontoxic, [PERSON] et al. [4] performed a machine learning study to classify molecules by identifying features that make them toxic. They also addressed the classification of the same molecules based on their effect on CRY1. While both problems are considered challenging, only the second has been further investigated in other studies [5].  
The problem of learning to determine toxicity from these 171 molecules is complex, characterized by many features and few examples. As a result, it is considered ill-posed, as traditional statistical methods often struggle with insufficient data. In this context, overfitting becomes a significant risk, as models can easily memorize the training data instead of generalizing. Therefore, we classify it as an overfitting-prone problem. Tackling these handicaps constitutes an interesting challenge in machine learning [6]. These handicaps motivate us to apply improved automated feature selection while remaining fully aware of its limitations.  
In this paper, we reproduce the machine learning experimentation from [4], revealing its weaknesses and evaluating the use of a more advanced feature selection process based on genetic algorithm meta-heuristic search to achieve improved and more trustworthy results.  
The main contributions of this paper are as follows:  
* Demonstration of the need for validation: We make evident the importance of incorporating validation after training and feature selection to avoid overfitting. In addition, we provide reliable estimates of the best performance achieved with this dataset.
* Genetic algorithm for automated feature selection: We propose a genetic algorithm (GA)-based framework for automated feature selection, which achieves results comparable to or better than manual Recursive Feature Elimination (RFE), reducing human effort and bias.
* Test variance as a generalization criterion: We propose the use of variance across cross-validation folds as an additional objective during feature selection, promoting models that not only perform well but also generalize better across different data splits.