Artificial Intelligence-Based Anti-monkeypox Drug Discovery Solutions

Creative Biolabs is a leading biotechnology company focused on monkeypox research and innovative laboratory technologies. Our goal is to provide a complete solution for drug discovery to facilitate the development of future drugs.

Overview of Artificial Intelligence-Based Anti-monkeypox Drug Discovery

Drug design and development is an important area of research for pharmaceutical companies and chemical scientists. However, off-target delivery, low efficacy, time-consuming, and high cost are barriers and challenges that affect drug design and discovery. Artificial intelligence (AI) and machine learning technology play a crucial role in drug discovery and development. AI algorithms are increasingly being applied for rapid and cost-effective drug discovery. Through the introduction of AI tools and network medicine, there are good opportunities for drug discovery and development.

AI-Based Tools And Techniques For Drug Discovery

AI is playing an increasingly important role in drug discovery and design. In recent years, It has been applied to de novo drug design. De novo design is a computational approach that can design a wide range of protein structures and modify naturally occurring proteins to design new functional proteins. Several web-based tools, such as LimTox, admetSAR, pkCSM, and Toxtree, can help reduce the burden of standard toxicity testing. AI-based techniques have proved useful in the identification of reusable drug candidates. By applying a variety of supervised ML and DL algorithms to experimental data, these techniques proved to be more effective in identifying new antivirals. In addition, ML-based methods can be effectively used for biomarker identification and drug susceptibility prediction, and improve the clinical success rate.

Fig.1 Flowchart of methods to identify potential pandemic antiviral drugs using virtual screening. (Thomas, 2022)

AI-Based Anti-Monkeypox Drug Discovery Solutions at Creative Biolabs

AI is involved in every stage of the drug development process.

• Peptide Synthesis and Small Molecule Design

Peptides are increasingly being explored for therapeutic purposes as they can cross the cellular barrier and can reach the desired target site. In recent years, novel peptides have been discovered through AI by researchers.

• Identification of Drug Dosage and Drug Delivery Effectiveness

With the advent of AI, many researchers are using ML and DL algorithms to determine the appropriate drug dosage. AI-PRS is a neural network-driven approach that relates drug combinations and doses to efficacy through parabolic response curves (PRS).

• Predicting Bioactive Agents and Monitoring of Drug Release

Mathematical models such as Higuchi has been applied in drug discovery, and one of the most common practice has been the calculation of the drug loading capacity of the selected or screened bioactive molecule.

• Prediction of Protein Folding and Protein-Protein Interactions

A novel hierarchical model, PCA-Integrated Extreme Learning Machine (PCA-EELM), is an effective tool for predicting protein-protein interactions using only protein sequence information, with high output accuracy and short duration.

• Structure-based and Ligand-based Virtual Screening
• QSAR Modeling and Drug Repurposing

QSAR modeling is a computational method through which quantitative mathematical models can be established between chemical structures and biological activities.

• Prediction of Physicochemical Properties and Bioactivity

These AI-based tools are used to predict the biophysical and biochemical properties of compounds, including SMILE formats, molecular fingerprinting, Coulomb matrices, and potential energy measurements.

• Prediction of Mode of Action and Toxicity of Compounds
• Identification of Molecular Pathways and Polypharmacology

One of the important achievements of AI and ML algorithms in drug discovery and development is the prediction and estimation of the overall topology and dynamics of disease networks, drug-drug interactions, or drug-target relationships.

AI research has been hailed as the key to faster and more accurate drug development and precision medicine. Creative Biolabs can use our extensive experience in AI testing to develop the necessary analysis for your project. Please feel free to contact us for more about your anti-monkeypox drug discovery project.