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voinelioun

Voin L

@voinelioun

AI Drug Discovery Computational Biology Specialist

Francia
Inglés
Parte de la información aparece en idioma inglés.
Sobre mí
I am a computational biologist and AI specialist with over 8 years of experience at the intersection of machine learning and drug discovery. I have worked with leading biotech firms and academic research institutes to design AI-powered molecular pipelines and accelerate drug candidate identification.... Lee más

Habilidades

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voinelioun
Voin L
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Revisa mis servicios

Aplicaciones Full Stack Web
I will build ai powered admet prediction models bioinformatics systems for healthcare
Automatizaciones y agentes
I will build ai agents for scientific research automation qsar and molecular docking ml

Experiencia laboral

AIHR

Senior Computational Biologist & AI Specialist

AIHR • Tiempo completo

Feb 2014 - Present12 yrs 3 mos

Designed and deployed AI-driven molecular discovery pipelines to accelerate drug candidate identification and optimization. Applied advanced machine learning and deep learning techniques to analyze high-dimensional biological and chemical datasets. Collaborated with cross-functional teams of bioinformaticians, medicinal chemists, and pharmacologists to support target discovery and lead optimization programs. Built predictive models for molecular property prediction, toxicity assessment, and compound screening, improving early-stage drug development efficiency. Developed computational workflows integrating genomics, proteomics, and cheminformatics data for precision medicine and therapeutic research. Led research initiatives with biotech companies and academic institutions focused on AI applications in translational medicine and computational drug discovery. Automated data processing and model training pipelines using Python, TensorFlow, PyTorch, and cloud-based computing platforms. Published and presented research findings on AI-assisted drug discovery, molecular modeling, and computational biology methodologies. Optimized virtual screening and molecular docking workflows, reducing candidate evaluation time and increasing hit identification accuracy. Mentored junior researchers and data scientists on machine learning best practices, biological data analysis, and scientific software development.