Drug Development for Hematological Cancer Targets Using Asclepius
Authors: Crucitti Davide , Pérez Míguez Carlos , Piñeiro Fiel Manuel , Diaz Arias Jose Angel , Gómez Fernández Jesús , Mosquera Orgueira Adrian , Zeidan Amer M. 
Published: December 2024

Introduction
Hematological cancers have recently seen significant advancements in targeted treatment options; however, many types still lack effective therapies. There is a critical need for novel drugs targeting new biological pathways to provide clinicians with additional treatment alternatives. Recent years have witnessed rapid advancements in drug development methodologies, particularly with the integration of Artificial Intelligence (AI) for the design and in silico exploration of drugs, significantly accelerating the drug discovery process compared to traditional laboratory methods.
Objectives
This study aims to design methodologies for the exploration of novel inhibitors for various protein targets implicated in hematological neoplasms using Asclepius, an innovative framework that combines the rapid design capabilities of AI-based methods with the reliability of computational chemistry calculations to evaluate drug effectiveness.
Material and Methods
We developed Asclepius, an automated algorithm designed to prioritize drugs for specific biological targets. This framework integrates multiple computational methodologies to screen potential drugs, which are then considered for laboratory evaluation. Unlike other design frameworks, Asclepius automatically screens, designs, and optimizes drugs while considering inhibition effectiveness, ADMET, and synthetic cost. The system is fully automated and interacts with a centralized database where all results are stored for easy retrieval and comparison.We utilize a structure-based drug design approach, leveraging structural information of biological targets. We generate diverse structural conformations for each target using AlphaFold3, PyRosetta, and OpenMM, and design drugs using a combination of generative and reaction-based methods.
Results
For this study, we selected a set of relevant targets, including DDX41, RUNX1, BTK, BCL2, SHP2, and PRDM1. Structural information for these proteins was gathered and used to create plausible conformations through a blend of AI-based methods, distance-based methods, and molecular dynamics simulations with known inhibitors.
Asclepius operates in cycles, generating molecules screened for synthetic accessibility and evaluated through molecular docking. The best-performing molecules are identified, and commercially available analogs are selected for cost-effective acquisition. We then cluster the molecules based on structural similarity and shape overlap, with the most relevant clusters undergoing precise binding energy evaluations using Free Energy techniques.
We evaluated between 500,000 and 5,000,000 molecules per target within one month. For each target, we identified numerous novel inhibitors that significantly outperformed existing ones in computational simulations, with approximately 7% of the generated molecules showing superior performance on average. Most of these molecules are readily available either commercially or through simple synthetic steps, and their predicted ADMET profiles are overall satisfactory for initial exploratory screening.
Additionally, our system elucidated the mechanism of inhibition of DDX41 and identified inhibitors for PRDM1, a target with no previously known inhibitors.
Conclusions
The integration of Artificial Intelligence in drug design presents a transformative approach in rapid and automated drug discovery, demonstrating the potential to significantly expedite the identification of new therapeutic candidates for hematological cancers. By leveraging AI-driven methodologies, we can swiftly design a diverse array of molecules that target previously underexplored proteins, such as DDX41, RUNX1 and PRDM1.
Our results indicate that Asclepius can efficiently evaluate and optimize many compounds, identifying numerous novel inhibitors that outperform existing ones in computational simulations. These promising candidates are currently undergoing in vitro assays and cell culture evaluations, with results to be presented at ASH, along with findings on other targets. Future research will focus on developing these drugs and further refining the Asclepius framework and expanding its application to other diseases.