Biography:
Mohamed Helal is currently professor of Drug Design and Development at Zewail City, Egypt. He received his PhD in Medicinal Chemistry and Drug Design from the University of Mississippi, USA. Between 2010 and 2017, he has been an assistant professor of Medicinal Chemistry at the Faculty of Pharmacy, Suez Canal University. He has received several research grants from the STDF and NAS to develop dual acting anti-diabetic, anti-cancer, and agents for treatment of addiction. Dr. Helal also holds two certificates in the fields of “Active Learning” and “Research Ethics” from the National Academy of Sciences (NAS) and the American Chemical Society (ACS). In 2018, he received a Fulbright Grant as a visiting scholar at the Department of Medicinal Chemistry, University of Florida. Also, he recently served as a visiting professor at the University of Catania, Italy. Dr. Helal also has former and current affiliations with world scientific societies such as the American Chemical Society (ACS), AAPS, and Rho Chi academic chapter of Mississippi. He has recently received the “research Incentive Award” of Zewail City.
Abstract:
AI-Driven Pipelines for Accelerating Lead Compounds Discovery
Artificial intelligence (AI) plays a crucial role in virtual screening for drug discovery by significantly accelerating the identification of potential drug candidates. Traditional screening methods, which rely on high-throughput experiments, are often time-consuming and costly. By leveraging AI, researchers can efficiently filter and rank promising candidates, optimize drug-like properties, and even design entirely new molecules, ultimately expediting the drug development process while lowering costs. Tools for deep neural networks and machine-learning models, such as DeepChem, Scikit-learn, and Keras have started to appear more frequently in scientific literature, gradually replacing the classical QSAR approaches. On the other hand, recent advancements in deep generative architectures have transformed de novo multi-target drug discovery by enabling controllable creation of molecules with balanced potency, selectivity, and safety profiles. Unlike traditional screening, generative methods shift the operational paradigm from selection to creation, directly navigating the vast, theoretically near-infinite chemical space (~1060 small molecules) to engineer novel ligands tailored to specific multi-target profiles. In addition, Cloud-based platforms have revolutionized lead discovery by offering scalable computational resources. Services like Google’s AlphaFold (for protein structure prediction), IBM’s RXN for Chemistry (AI-powered synthesis planning), Reaxys (Elsevier), and AWS-powered Schrödinger LiveDesign facilitate drug design and large-scale VS studies. Moreover, services like Google Colab and Knime allow researchers to run machine-learning models and conduct extensive screenings without requiring expensive local infrastructure, making drug discovery more accessible. Overall, these advances are transforming drug discovery by improving hit identification, reducing experimental costs, and accelerating the development of new therapeutics.