## 3 QSAR Today
### Data Partitioning: Set Aside a Portion of the Data for External Validation
In contrast, in supervised learning, the system is trained on input-output pairs, enabling it to predict correct outputs for new inputs. Popular ML techniques in QSAR modeling include k-nearest neighbor (k-NN) [136], Artificial Neural Networks (ANNs) [137], Support Vector Machines (SVMs) [138; 139], Decision Trees (DTs) [133; 134], Random Forest (RF) [140], XGBoost [141], etc. Machine learning (ML) and Deep Learning (DL) are subfields of Artificial Intelligence (AI), each specializing in different aspects of data processing. The primary distinction between ML and DL lies in the scale and complexity of the data they handle. ML typically works with smaller datasets and focuses on decision-making and problem-solving for large but manageable datasets. It employs algorithms that detect patterns in categorized data, improving their accuracy as they process more information over time. Deep Learning, a subset of ML, utilizes neural networks with multiple hidden layers to analyze and learn from vast and complex datasets. These advanced neural networks are designed to capture intricate relationships and functions within the data. AI, as an overarching concept, encompasses technologies that simulate human intelligence, enabling machines to sense, reason, act, and adapt, effectively replicating or imitating human cognitive abilities [142; 143; 144]. Each method has specific parameters that can be tuned to improve model performance. The objective is to optimize these settings to create models that accurately predict the properties or activities of previously unseen compounds. A model that is too simple may underfit the data, while an overly complex model may overfit, performing well on training data but failing to generalize to new data. Feature selection minimizes the descriptor set, retaining only those essential for defining the model. For \"global\" QSAR models, which may involve hundreds or even thousands of compounds, simple descriptors are generally preferred due to the model's inability to capture the full complexity of molecular interactions. Examples of such simple descriptors include molecular weight, logP, polar surface area, and the number of hydrogen bond donors and acceptors. \begin{table}
\begin{tabular}{c c} \hline \hline
**Unsupervised ML** & **Supervised ML** \\ \hline \multirow{5}{*}{PCA [39; 40; 115] Clustering [119]} & MLR [121] \\  & PLS [122; 123] \\  & Counter-propagation networks [131] \\  & Genetic algorithms (GAs) [132] \\  & Decision Trees [133; 134] \\  & Back-propagation network [135] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Unsupervised and supervised ML methods employed in QSAR modeling. In contrast, \"local\" QSAR models require more detailed descriptors. These models are typically used in lead optimization projects where improving specific molecular properties is essential. Therefore, more comprehensive descriptors related to the chemical structure are necessary, enabling targeted suggestions for structural modifications [145]. There are three main methods for feature selection: filter, wrapper, and embedded methods (Table 2). Filter methods select a subset of descriptors based on the intrinsic properties of the data [146; 147]. They use relevant scoring metrics to rank features and select the top-ranked ones. Wrapper methods apply a learning algorithm to evaluate different subsets of features [148]. They conduct a search within the space of possible parameters, selecting features that optimize the model's performance. Embedded methods, also known as hybrid methods, integrate feature selection into the training process of a specific learning algorithm. Like wrapper methods, they are sensitive to the structure of the underlying classifier and are tailored to a particular learning algorithm [149].