Identifying new and better medicines for treating human diseases is a critical and time-consuming process. Drug discovery services companies are always experimenting to find new compounds with effective therapeutic qualities. But the long and complex process makes it necessary to accelerate the pace so that pharmaceutical companies can generate sufficient returns to sustain drug research and development.

Artificial Intelligence (AI) and Machine Learning (ML) are path-breaking technologies that are helping drug discovery organizations drive their digital transformations. AI-based tools are being applied to almost every stage of drug development to minimize time. Biological and chemical sources provide large volumes of complex information. Analyzing large and varied data sets is a time-consuming and tedious procedure. Applying machine learning algorithms to the process allows researchers and organizations to make rapid evaluations. Let’s dive a bit deep into the topic to learn how drug discovery organizations are benefiting from AI.

1. Using AI In Target Identification And Validation

When conducting target identification, drug discovery service providers try to establish a causal association between the target and the disease. They need to demonstrate that a target when modulated impacts the disease through a naturally occurring variation or an experimental intervention. ML algorithms are being used to analyze large genetic datasets to make predictions about potential causality. AI and natural language processing (NLP) are also being used to scan existing literature and other written material to look for gene-disease links and determine new targets. ML algorithms are being used to power AI tools that can automate the transformation of diverse biomedical and healthcare data into computer models that resemble individual patient conditions.

2. Leveraging Machine Learning For Small-molecule Design And Optimization

Drug discovery laboratories in India or elsewhere have to conduct virtual and experimental high-throughput screening of large compound libraries on a large scale to determine drug candidates with the ability to block or activate the target protein of interest. These small molecules undergo refinement to improve target specificity and selectivity. Understanding the properties and activities of small molecules to identify effective lead compounds has become easier with multi-task deep learning models. The technique has been refined during the past few years and is now able to reduce the computational toll while evaluating a large dataset for predicting a molecule’s readout. Some enterprises are using state of the art generative models for feature extraction. These, when combined together with reinforcement learning have helped generate massive results while designing compounds with ideal values for pharmacokinetic and pharmacodynamics properties.

3. Drawing New Insights From Biomedical And Clinical Data

AI-based models are being employed to analyze biomedical and clinical data. This is helping drug discovery professionals to draw new insights about drug candidates. AI models are also enabling researchers to determine novel pathways, targets, and biomarkers by simulating entire biological systems. The models can be applied to biomedical data such as protein interaction networks, gene expression measurements, and clinical records. Examining numerous points of information rapidly and with maximized accuracy has become possible for drug discovery researchers. They can now determine relevant associations pointing at the effectiveness of specific molecules. Drug discovery screening services can now screen biomarkers and all ‘omic data related to a specific patient population. This will help in the development of improved medication for such patient groups.

4. Supporting Multi-target Drug Discovery Approach

For long, drug discovery services companies have used the one target for one disease approach. However, as time progressed many organizations realized that this strategy is not efficient for finding medications for complex diseases. For instance, a person suffering from diabetes has to take care of his glucose levels and body weight besides comorbidities like nonalcoholic steatohepatitis (NASH). The multitarget approach can be used to determine a pair of targets that can handle these areas. This is the reason why the multitarget model has been adopted by many organizations to design medicines with higher efficiency. AI models are being used for discovering and developing bispecific small molecules that can treat a complex disease and its comorbidities.

5. Helpful In Rapidly Identifying Predictive Biomarkers

Biomarkers are measurable indicators of the presence or the severity of a disease. Contrary to popular belief biomarkers are critical not only for medical diagnostics but also for drug discovery services agencies in India and other locations. Drug discovery researchers can use them for purposes such as identifying potential responders to molecular targeted therapy before the process moves to the human testing phase. Drug sensitivity predictive models can be used to analyze preclinical data sets and rapidly predict a translational biomarker. Once a model and its associated biomarker have been validated through clinical or preclinical data sets, they can be used for identifying potential indications and suggesting the drug’s action mechanism.

6. Applying Deep Learning To Computational Pathology For Faster Results

The descriptive field of pathology generates a vast amount of information that is sometimes impossible to acquire from other means. For instance, experts working in the field of drug discovery for oncology are interested in the interactions between tumor and immune cells within a tumor microenvironment. They are dependent on pathology techniques as the behavior cannot be captured by any other technology. Furthermore, they need to assess the impact of drug treatments on specific tissues and cells. They need to test a large number of compounds before identifying a candidate for a clinical trial. Applying deep learning algorithms to computational pathology will allow researchers to conduct the tests rapidly and with improved accuracy. This will reduce drug development time and allow therapies to reach patients quickly.

Collaborate With Integral BioSciences For Path-breaking Drug Discovery Services

Artificial Intelligence has made a significant impact in many other industries but the pharmaceutical sector was late in adopting this innovative technology. However, various organizations in the industry have now started employing AI-based tools and deep learning models to improve the space and accuracy of complex and iterative processes. Integral BioSciences is an esteemed drug discovery incubator that does not shy away from using technological innovations. It is one of the few drug discovery services companies that employ state of the art tools to provide efficient services. The contract research organization in India regularly partners respected pharmaceutical and healthcare enterprises on intricate projects. Contact Integral BioSciences for high-quality drug discovery services in major therapeutic areas.