The Insilico Lab Partners with Xcene Research for the SCD In Silico Track

The Insilico Lab Partners with Xcene Research for the SCD In Silico Track at SCD-ASS 2026

The Insilico Lab was proud to serve as an official partner of the Sickle Cell Disease African Scientific Symposium (SCD-ASS) 2026, organized by Xcene Research and held on July 30–31, 2026, at the Marriott Hotel, Ikeja, Lagos.

Under the theme “Accelerating Drug Development for Sickle Cell Disease in Africa,” the symposium brought together researchers, healthcare professionals, scientists, industry stakeholders, and other experts working toward advancing sickle cell disease research and drug development across Africa. As part of the symposium, The Insilico Lab led the “SCD In Silico Track,” introducing participants to the role of computational approaches in modern drug discovery and their potential application to sickle cell disease research.

Introducing Computer-Aided Drug Discovery

The SCD In Silico Track provided participants with an introduction to the computational drug discovery pipeline, demonstrating how publicly available databases and computational tools can be integrated to investigate potential therapeutic compounds.

The sessions covered key stages of Computer-Aided Drug Discovery (CADD), including:

• Protein Data Bank (PDB): accessing and exploring experimentally determined protein structures.

• PubChem: retrieving chemical structures and molecular information for potential compounds.

• Binding-site identification: understanding protein binding pockets and their relevance to drug discovery.

• Drug-likeness screening: evaluating compounds based on key physicochemical and drug-likeness properties.

• ADMET prediction: introducing the assessment of absorption, distribution, metabolism, excretion, and toxicity properties.

• Molecular docking: exploring how computational docking can be used to predict ligand–protein interactions and prioritize potential drug candidates.

Through practical demonstrations and discussions, participants were introduced to how these computational methods can help shorten the early-stage drug discovery process, prioritize promising molecules, and generate hypotheses for experimental investigation.

Building Africa's Computational Drug Discovery Capacity

The SCD In Silico Track reflects The Insilico Lab's commitment to expanding access to computational drug discovery education and skills development, particularly among researchers and students across Africa and globally.

As drug discovery increasingly integrates computational methods with experimental approaches, developing local expertise in bioinformatics, chemoinformatics, molecular modelling, and AI-driven drug discovery will be important for strengthening Africa's capacity to contribute to the development of therapeutics for diseases that disproportionately affect African populations.

 Our participation in SCD-ASS 2026 therefore represents more than an event engagement. It is part of our broader mission to equip the next generation of African researchers with the computational skills and resources needed to participate in modern drug discovery.

A Collaborative Step Toward In Silico Drug Discovery for Sickle Cell Disease

We are grateful to Xcene Research for the opportunity to contribute to SCD-ASS 2026 and for recognizing the importance of computational science within the broader drug-development ecosystem.

Through initiatives such as the SCD In Silico Track, we hope to encourage greater integration of computational and experimental approaches and support the growth of an African scientific community capable of driving innovative solutions to sickle cell disease and other health challenges.

The Insilico Lab remains committed to advancing computational drug discovery through education, research, collaboration, and open access to scientific resources.

Members of The Insilico Lab team facilitating the SCD In Silico Track at the Sickle Cell Disease African Scientific Symposium 2026. From left: Ms. Temitayo Abidat Hassan, Director of Operations; Mr. Sheriffdeen Oladimeji Hammed, Research Associate; and Ms. Maryam Zubair, Research Associate.