A model, in AI and machine learning, represents a mathematical or computational tool that is created to simulate, predict, classify, or make decisions based on a certain set of inputs.
Models are abstract representations of the real-world phenomena or systems, which are built using algorithms and data.
They serve as a basis for understanding the underlying system or process, predicting future events, or deciding optimal actions.
Models can vary based on the type of algorithm used, the kind of data they process, their complexity, their output, etc.
Modeling is applied in various scenarios where patterns need to be identified, predictions need to be made, or decisions need to be taken.
i) Predictive Modeling for Compound Activity: In drug discovery, predictive modeling can be used to predict the biological activity of chemical compounds. For instance, QSAR (Quantitative Structure-Activity Relationship) models relate chemical structures to their biological activity. These models use features derived from the molecular structure to predict the compound's activity, significantly speeding up the drug discovery process by eliminating less promising compounds early.
ii) Simulation Modeling for Biological Systems: Models can simulate complex biological systems at the molecular or cellular level. For example, models can help understand protein-ligand interactions, helping researchers visualize how a drug might interact with its target in the body.
iii) Decision Models for Optimal Experimental Design: Decision models can assist in designing experiments for drug discovery. For instance, they can help in selecting the most promising drug candidates for clinical trials based on various parameters like predicted efficacy, safety profile, etc.