The roles of ML and DL in advancing pharmaceutical processes, with special attention to their demanding applications in drug discovery, optimizing manufacturing workflows, ensuring consistent QC, and designing targeted drug delivery systems.
High-Throughput Screening (HTS)
HTS enables scientists to test thousands to millions of compounds to determine whether they interact with specific biological targets of interest. This was achieved using ML models that predict the bioactivity of any compound, thereby significantly reducing the need for rigorous physical screenings. The virtual screening method utilizes technologies such as AutoDock, DeepChem, and Docking Score ML® to examine large compound libraries for potential molecules.
Structure-Based Drug Design (SBDD)
SBDD is a technique that employs the three-dimensional structure of target proteins to develop new drugs. The behavior of a potential drug is predicted using deep learning (DL) models for its interaction with these proteins, including its binding affinity and specificity. SBDD can help identify lead compounds that need to be further improved by building and testing iterations.
De Novo Drug Design
Generative adversarial networks (GANs) are generative models that are trained to generate new molecules with one or more specific properties inherent to them. This makes it possible for GANs to generate compounds with the required biological activity and pharmacokinetic attributes because the model is built on existing chemical data.
Reinforcement learning (RL) enhances drug design because the designer cycles through new molecular structures of the drug, making improvements based on the results of the interaction simulation with biological targets. The use of DL can extend enormous chemical spaces, which can help identify drug prospects. De novo drug design tools, such as GANs, RL, simplified molecular input line entry systems (SMILES®), and POLYGON®(a polypharmacology approach based on generative RL), are used to design novel chemical structures and generate multi-target compounds.
Adverse Drug Reaction Prediction
Large-scale ML platforms consider aspects such as a patient’s EHRs and clinical data to estimate the propensity for ADRs. With the help of this type of model, high-risk medications and patients can be detected by analyzing large numbers, which can enhance patient safety.
Tools for predicting ADRs include Tox21, which uses ML to assess drug toxicity; DeepADR for predicting ADRs from drug features and protein interactions; MeDeA, which analyzes clinical data to predict ADRs; the FDA’s FAERS, which is integrated with ML for ADR detection; and the pharmacovigilance database VigiBase, which employs data mining for ADR prediction.
Predictive Biomarkers
The DL algorithm can learn from other biomarkers to predict a patient’s response to a specific treatment. These biomarkers are useful for developing patient-specific treatment plans, enabling clinicians to select the best treatment modalities for patients.
Read also: Artificial Intelegence in Drug Manufacturing
