Artificial Intelligence in Drug Discovery

AI continues to streamline several processes in the medical field. Artificial intelligence in the medical field has already been adding value to medical diagnostics. In the sphere of drug discovery, it is being implemented majorly in Immuno-oncology, Neurodegenerative Diseases, and Cardiovascular Diseases. By 2024, drug discovery will climb to a staggering above 40% CAGR in the four main regions of the world.

Why AI in Medicine?

Commenting on the estimations of the same, the global drug discovery market is chunked into four major regions - Europe, APAC, North America, and the rest of the world. Drug manufacturing is expanding at a rapid pace, riding high on the growing population and increasing the financial capacities of patients.

AI has accelerated the detection of cancer cells, diabetic retinopathy, and skin lesions for breast cancer from images, saving medical practitioners and radiologists from conducting laborious processes for suggesting treatment. Highly efficient algorithms combined with training data for medical AI applications are turning the way around in the medical field.

Drug discovery, being a costly process to manage and expand, has now been explored using artificial intelligence in medical field. Plus, the process of developing medicines for specific medical conditions is nefariously time-consuming.

Manufacturing of drugs involves the following stages:

Identifying target conditions

Checking the consumption potential

Manufacturing of the drug

Conducting clinical trials

Biomarker for disease diagnosis

Once a drug is discovered and the biomarker has been identified, more personalized treatment can be devised for patients. Machine learning algorithms are being employed to understand patients’ conditions and symptoms and find out biomarker responses to drugs. As per which, the diagnosis is suggested by the AI computational model. Simultaneously, there are numerous data challenges that have prevented drug manufacturers from adopting AI in drug discovery and personalized drug development, often called precision medicine.

Types of Data Supported at Cogito for Medical Image Annotation

Furthermore, AI can also support drug design on a large scale by predicting the 3D structure of the target protein, and its interaction with the protein. While it can add value to drug repurposing approaches by identifying the new therapeutic usage of the drug. Through drug screening, medical AI applications can help understand the bioactivity levels in a drug, physicochemical properties, and classification of target cells for further screening.

Challenges in the Way

In the present scenario, there are more barriers than possibilities. Limited amounts of data and ML models have been able to support what is needed in developing the challenges of precision medicine.

For the course of AI in medicine and drug discovery, Quantitative structure-activity relationship (QSAR), a vital parameter to define physicochemical parameter or number of compounds, lacks the amount of training data for computational machine learning models to predict results. On the other hand, the efficacy of several predictive models is being examined in depth to check molecular similarity, molecule generation, and various silica approaches in the process of finding out the chemical composition. Similar kinds of implementations are being sought in polypharmacology, chemical synthesis, drug repurposing, and screening tasks.

To quote, in terms of predicting drug-protein interaction for drug-repurposing, AI based support vector machine models are being utilized to find out and elaborate ligand-protein interaction for maximum efficacy. In a broader sense, adhering to strict guidelines for breaking down medical processes and exploring solutions within a timeframe remains a key obstacle; medicine being a life-sensitive area.

Also Read: Top Five Best Usages of Artificial Intelligence in Healthcare Medical Imaging

The Way Ahead

The scope for AI has continued to broaden. The responsibility of patient profiling for clinical trials of a new drug as per genome–exposome profile analysis for suitable patients has the potential to critically reduce the cost of re-manufacturing drugs. AI-backed drug repurposing is helping manufacturers save a whopping US$41.3 million by directly launching the re-prepared drug for clinical trials using AI models.

When it comes to Artificial Intelligence, solving micro-level blockages is imminent. AI can play a paramount role in drug discovery and the subsequent manufacturing of precision medicine. In the matter of Quantitative structure-activity relationship (QSAR), modeling tools are being identified and tested leading to evolved AI-based QSAR approaches, using machine learning algorithms and speeding up further analysis of the procedure.

Deep learning has equally been adding to ML models by quantizing the findings of algorithms. A close watch on what AI can unravel in medicines will be both beneficial and mandatory for new advancements in the field.