Development of Molecular Formulations that become drugs to treat or cure diseases is the heart of the Pharma industry. > Pharma spends ~15% of sales on R&D Computational Chemistry's Digital Tools: - Molecular Dynamics (MD) Simulations - [[Density Functional Theory (DFT)]] called Computer-assisted Drug Discovery (CADD) Benefits of Quantum Powered CADD: 1. Save time & get accurate predictions - for medium to large sized molecules + shorter screening time: 2. Reduce no. of development cycles and eliminate dead-ends which are costly - Accurate predictions - Modeling process impacts --> How proteins fold, how drug candidates interact with biologically relevant proteins - Restrictions to finding the best drug candidates: - restrict the structural flexibility of the target molecule due to a lack of computational power and a limited amount of time > Tackling complex and larger molecular systems, which can't be replicated with either HPC or today's NISQ Development & Manufacture of Molecules **Identify and develop small molecules and macro-molecules** [[Molecules]] = quantum systems, based on quantum physics Showcase how we can **predict and simulate** the structure, properties and behavior (reactivity) of these molecules better than conventional computers Exact methods = computationally intractable Approximate methods = not sufficiently accurate when the interactions on the atomic level are critical QC have the capacity to simulate the complete problem, including **interactions on the atomic level.** [[Quantum Pharma Use Cases]] [[Drug Discovery]]