AI Use Cases 

Image analysis or GCMS analysis in a high throughput manner. Applications of GCMS include drug detection, fire investigation, environmental analysis, explosives investigation, and identification of unknown samples.

Analyse candidate patient data to measure probability of individual resistance to deployed drugs, typically projected over time and across a broader population.

Continual research and observation of the drug's effects on patients after the drug becomes generally available

Predicting prescription adherence with different approaches to reminding patients

Analyse relevant biomarkers to evaluate potential medical applications and outcomes. Identify which genes potentially cause which disorders to simplify diagnosis of patients and provide insights into the functional characteristics of the genetic mutation.

Predict outcomes from fewer or less diverse experiments to reduce R&D costs and time to market - potentially also mitigating cost and loss of life from animal experimentation

Predict how successful CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) will be at editing the targeted genome

During the initial phase of drug research and development, the target for a new drug treatment must first be chosen. The target molecule will be what the drug compound interacts with to get the intended outcome, often the treatment of a disease.

Analyse relevant characteristics and identify potential individuals to be recruited from a relevant population to serve in drug testing trials. These trials may be different stages in the drug development process.

Leveraging molecule database with metabolic stability data to elucidate new stable structures

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Identify drug compounds with current regulatory approval which could be used in new ways to treat other conditions. Machine learning assists by searching through existing research literature for known and inferred relationships

Once a drug target has been identified, the drug compound itself must be evaluated for safe use in living organisms before live testing can begin. This includes researching how the drug will be metabolised by the body and identifying potential toxic interactions and side effects.

Analyse the data set (cost / benefit analysis) of potential development projects to assist with prioritisation of research effort and resource allocation in the product development process.

Analysis of clinical outcomes to adapt clinical trial design

Identifying biomarkers for boxed warnings on marketed products. Drug labelling may contain information on genomic biomarkers and can describe issues such as drug exposure and clinical response variability, risk for adverse events, genotype-specific dosing, mechanisms of drug action, polymorphic drug target and disposition genes, and trial design features.

Optimise the testing and manufacturing processes to enable efficient turnaround and throughput for drugs to be trialled during the research and development phase.

Combining medications can produce negative side effects - and potentially mitigate the positive impact. Issues for this include limited overlap case studies, decentralised information and unclear cause and effect. Using AI on appropriate data sets can uncover previously unnoticed correlations. Note that 11% of the US population claim to have used at least 5 medications in a given 30-day period.

Researchers are taking advantage of computational power to analyse vast amounts of data on drugs and their interactions. Enhancing the drug research process by advancing data mining, integration and analysis as well as testing can result in quicker drug and cure discovery for known diseases.

Scan social media to discover references to product and competitors for product management purposes. This may include customer sentiment analysis on key product or service attributes.

Automate generation of articles - for example using national data sets and then producing local variant articles from this. The same can be done for press releases.

Successful social media sites tend to have more content available for users than all but the most addicted (and socially restricted) could possibly read. This means that they have to use multiple indicators to power the algorithms that rank the content displayed. In this they are in a constant battle to stay one step ahead of the content marketeers trying to game the system.

Improving audio quality - for example by eliminating background noise or static - has numerous follow-on applications: helping deliver better outcomes from translation or hearing impairement support devices, delivering cleaner data for NLP products and providing better consumer experiences.

Predict outcomes using fewer experiments to reduce experimental R&D costs. Examples would include simpler component testing and using models to minimise the requirement for expensive and time-consuming track testing.

Predict new high value crop strain performance based on past crop trends, weather and soil data. Effectively speeds up traditional farming innovation but can face some PR challenges.

During the initial phase of drug research and development, the target for a new drug treatment must first be chosen. The target molecule will be what the drug compound interacts with to get the intended outcome, often the treatment of a disease. Researchers use deep neural networks to predict molecular level interactions to treat a condition or improve particular functionality

Machine learning can be used to speed up the product research and development phase of the chemical industry.

Predict real-world results from fewer experiments to reduce experimental R&D costs (e.g., new crop testing). This can have a positive environmental impact.

Monitor and analyse interactions with customers (potentially across all channels ranging from market research to social media to direct contact) to create insights to improve product offering. The aim is to ensure that there is a positive feedback loop in to product development.

Personalise product offerings to target individual consumers based on multiple data sources (e..g mobile, social media, location, etc.) and historic / comparable behaviour patterns.