Feb. 14, 2024, 5:43 a.m. | Mohammad Ghazi Vakili Christoph Gorgulla AkshatKumar Nigam Dmitry Bezrukov Daniel Varoli Alex Aliper D

cs.LG updates on arXiv.org arxiv.org

The discovery of small molecules with therapeutic potential is a long-standing challenge in chemistry and biology. Researchers have increasingly leveraged novel computational techniques to streamline the drug development process to increase hit rates and reduce the costs associated with bringing a drug to market. To this end, we introduce a quantum-classical generative model that seamlessly integrates the computational power of quantum algorithms trained on a 16-qubit IBM quantum computer with the established reliability of classical methods for designing small molecules. …

algorithm biology challenge chemistry computational computing costs cs.ce cs.gt cs.lg development discovery drug development generative molecules novel process quant-ph quantum quantum computing reduce researchers small

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Data Engineer

@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania

Artificial Intelligence – Bioinformatic Expert

@ University of Texas Medical Branch | Galveston, TX

Lead Developer (AI)

@ Cere Network | San Francisco, US