AI-Driven Drug Screening

Virtual screening relies on computer simulations and molecular docking methods to evaluate and predict the biological activity of various compounds. Artificial Intelligence (AI) drug screening is a high-throughput screening method that integrates AI technology with computational chemistry, extensively utilized in areas such as protein structure prediction, new drug development, and molecular design and optimization. AI screening leverages machine learning (ML) algorithms to analyze vast datasets, identify patterns, and generate AI scoring functions. This approach enhances screening efficiency and accelerates the discovery of potential drug candidates.

MCE AI drug screening platform integrates various advanced methodologies, including molecular docking, deep learning, and molecular dynamics simulations. By utilizing high-performance servers, it can efficiently screen tens of millions of molecules within just a few hours, thereby facilitating truly effective drug screening.

Figure 1. Application of AI Technology in Drug Discovery.

Target-based AI screening

Target-based AI screening employs algorithms such as deep neural networks and random forests from machine learning, along with techniques like molecular docking, to develop models that elucidate the relationship between the chemical structure of compounds and their biological activity. This approach facilitates rapid predictions of the mechanisms of action for drug compounds. By taking the prediction of protein-small molecule binding using deep learning (DL) models as an example, it illustrates the process of target-based AI screening.

Figure 2. An overall flowchart for predicting protein-ligand interactions based on DL models.

Data
Collecion

Feature
Extraction

Model
Training

Binding
Prediction

•Protein structure and small molecule compound data, including structural and biological activity information, are gathered from publicly available datasets such as PDBbind, ChEMBL, and RCSB PDB.

•These data serves as input for the model.

• Feature extraction involves converting raw data into a format suitable for processing by deep learning models.

• For example, molecular fingerprints can be used to represent the structure of small molecules, while the features of proteins can be encoded using their amino acid sequences or three-dimensional structures.

•Commonly used deep learning models include Convolutional Neural Networks (CNN), Graph Neural Networks (GNN), and Transformer models.

•These models learn and identify potential binding patterns by comparing known protein-small molecule binding instances.

•During the training process, the models continuously optimize their parameters to enhance the accuracy and reliability of their predictions.

• Input the small molecules to be screened into the trained model to predict their binding affinity with the target protein.

• Based on the prediction results, rank the small molecules and select the top candidates for experimental validation.

Ligand-based AI screening

Researchers can search existing compound libraries for molecules with desired properties by using ligand-based AI screening, or utilize known active compounds as a training set to summarize their characteristics using AI tools, thereby generating similar novel molecules. AI generative models can explore a wider chemical space to identify novel compounds and design candidates with specific drug-like characteristics, thereby ultimately improving the efficiency and success rate of drug development.

Figure 3. Graph neural networks predict the chemical properties of more than 10⁹ molecules in silico.

Service Advantages

• One-stop services for ligand/receptor-based AI screening, molecular dynamics simulations, structural optimization, and compound synthesis.
• Robust capabilities in chemical synthesis and a variety of comprehensive synthesis techniques.
• High-performance computing servers guarantee rapid and efficient data processing.
• Professional team specializing in molecular simulation and drug design, backed by extensive industry experience.
• Rigorous data privacy management standards guarantee the information security.

Quote for Service

The MCE AI-Driven Drug Screening service requires evaluation to determine the appropriate scheme and pricing. For additional information about service price and technical specifics, please send an email to sales@MedChemExpress.com or contact the sales staff of MedChemExpress directly.

Compound
Screening Libraries

Download

Compound Customization

Specific Compounds
Quantities
Plate Map
Concentration
Form (Solid or Solution)
YES

Name
Email *

	Sorry, but the email address you supplied was invalid.