From Years to Hours: Accelerating Drug Discovery with Advanced ML Techniques

Courtesy Photo | General rational pipeline for antimicrobial peptide (AMP) prediction using GDL: From an initial AMP amino acid sequence, the relevant physicochemical characteristics are extracted, and the 3D structure of the sequence is predicted. Once the sequence and spatial relationships are obtained, they are converted into graphs in which the structural information is represented by the edges, while the amino acid residue information is represented by the nodes. The graph-based data is presented to a GDL network to predict whether the candidate is likely to have antimicrobial activity. (UPenn image created with BioRender.com)  

FT. BELVOIR, VIRGINIA, UNITED STATES

08.12.2024

Courtesy Story

Defense Threat Reduction Agency's Chemical and Biological Technologies Department

To help remedy the cumbersome, time-consuming, and expense of creating new drugs against biological pathogens, research into artificial intelligence (AI), machine learning (ML), and deep learning (DL) technologies have allowed scientists to focus on rapid drug design and development by combining DL with the principles of geometry to reduce failure rates, length of schedule, and cost-associated risks of drug development.

It is increasingly difficult to develop medical countermeasures (MCMs) against the biological pathogens available to our adversaries. To deter these biological pathogens against the Joint Force and maintain a scientific and technological edge, the Defense Threat Reduction Agency’s (DTRA) Chemical and Biological Technologies Department in its role as the Joint Science and Technology Office (JSTO) for Chemical and Biological Defense, an integral component of the Chemical and Biological Defense Program, invested in basic research with the University of Pennsylvania (UPenn) to rapidly create MCMs using aspects of machine learning.

The traditional drug discovery, design, and development process uses taxing laboratory tests and experiments to evaluate the safety and effectiveness of new drugs. Those with the desired traits are further evaluated in multiple clinical studies before achieving regulatory approval.

To help speed up the process, researchers at UPenn developed a model system that uses DL to identify new antimicrobial peptides (AMPs), which are a class of natural peptides that exhibit antimicrobial activity characterized by broad-spectrum actions including antibacterial, antifungal, antiviral, and antiparasitic effects. In addition, AMPs have low molecular masses and often possess high antimicrobial, antibiofilm, and anti-inflammatory properties.

The UPenn researchers used several DL methods to predict, identify, design, and develop AMPs that offer protection against bacterial pathogens which cause human diseases. Even though they were promising while needing further development, these first-generation computational tools did not include the precise 3D properties of AMPs to improve the accuracy of peptide binding properties, target affinity, and drug-like efficacy.

To overcome this hurdle, the UPenn researchers harnessed Geometric Deep Learning (GDL) that combines DL with the principles of 3D geometry to predict AMPs that possess high degrees of target specificity and affinity, which translates into greater antimicrobial potency. Also, GDL can capture the complex relationships between geometric properties and function of these molecules, as it more adequately represents the 3D non-Euclidean nature of peptide structures than traditional machine learning methods that operate in the 2D Euclidean spaces.

In contrast to traditional DL methods, GDL considers the fundamental structural features in three ways:

• Date Taken: 08.12.2024
• Date Posted: 08.12.2024 22:58
• Story ID: 478446
• Location: FT. BELVOIR, VIRGINIA, US