Use Case Prioritization

One of the main challenges in drug discovery is to find molecules that bind specifically and strongly to their target protein while having minimal binding to other proteins. By predicting binding affinity, it is possible to identify the most promising candidates from a large pool of potential compounds, reducing the number of compounds that need to be tested experimentally.

Despite significant progress has been made to facilitate learning in CNNs, such as via pre-training, the AFs used in CNNs and related architectures have remained almost unaltered. The continued reliance on non-optimal AFs has impacted the performance of CNNs for medical diagnostic applications. This work is focused on DL-based image classification to aid medical diagnosis of COVID-19 and Parkinson’s Disease (PD).

There are several different mechanisms responsible for the release of cfDNA in blood plasma, and henceforth it can provide information regarding dynamic changes in the human body. Due to the fragmented nature, low concentration of cfDNA, and high background noise, there are several challenges in its analysis for regular use in diagnosis of cancer. Such challenges in the analysis of the methylation profile of cfDNA are further aggravated due to heterogeneity, biomarker sensitivity, platform biases, and batch effects. This review delineates the origin of cfDNA methylation, its profiling, and associated computational problems in analysis for diagnosis.

Protein folding problems involve finding the lowest energy configuration of a protein’s amino acid sequence and is a computationally challenging optimization problem that is important in fields such as chemistry, biology, and drug discovery. Much research has been done so our focus on this would be to further validate the current approaches, see if there are any improvements and provide a path to scale.

Identifying and developing small molecules and macromolecules that might help cure illnesses and diseases is the core activity of pharmaceutical companies. Given its focus on molecular formations, pharma as an industry is a natural candidate for quantum computing. The molecules (including those that might be used for drugs) are actually quantum systems; that is, systems that are based on quantum physics. QC is expected to be able to predict and simulate the structure, properties, and behavior (or reactivity) of these molecules more effectively than conventional computing can. Exact methods are computationally intractable for standard computers, and approximate methods are often not sufficiently accurate when interactions on the atomic level are critical, as is the case for many compounds. Theoretically, quantum computers have the capacity to efficiently simulate the complete problem, including interactions on the atomic level.