AI-Driven Drug Discovery

AI Accelerates Key Stages of Drug Discovery

AI is transforming drug discovery by making it faster, more efficient, and more cost-effective. Below are the critical phases where AI-based predictions have the greatest impact:

• Hit identification: Predicting drug–target binding affinities

• Lead optimization: Predicting ADMET properties

• Safety profiling: Predicting potential side effects ahead of clinical trial initiation

Learn more about our omics analysis services supporting other stages of drug discovery, including target discovery and mechanism-of-action studies here.

Hit Identification

Once a biological target has been identified, large language model (LLM)-based AI tools can be used to perform in-silico screening and predict binding affinities across millions of drug-like molecules. These models are customizable—you may choose to focus on a specific target, a target family, or a disease-associated pathway.

The only input required is the FASTA sequence of the target. Our models then identify candidate molecules ranked by their predicted binding affinity with the target/pathway. If the baseline model does not yield potent hits, we can build generative AI models to design novel compounds with optimized binding potential.

Hit-to-Lead Optimization

At this stage, we develop predictive models to evaluate the ADMET properties—Absorption, Distribution, Metabolism, Excretion, and Toxicity—of compounds identified in the previous stage. These models are essential for assessing whether a molecule has a favorable pharmacokinetic and safety profile, both critical for progression to later development stages.

Input to the models includes structural representations of lead compounds, such as SMILES (Simplified Molecular Input Line Entry System). The models are trained on large, curated datasets with experimentally validated ADMET annotations. Example endpoints include:

• Absorption: Human intestinal absorption (HIA), Caco-2 permeability
• Distribution: Plasma protein binding (PPB), blood-brain barrier penetration
• Metabolism: CYP450 inhibition
• Excretion: Renal clearance, half-life
• Toxicity: hERG inhibition, hepatotoxicity, mutagenicity (Ames test), Phospholipidosis

Depending on the endpoint, models may be structured as binary classifiers (e.g., permeable vs. non-permeable) or regression models (e.g., logBB, LD50). Multitask learning approaches can also be employed to improve generalization across related endpoints.

Early prediction of ADMET properties reduces experimental costs, flags high-risk compounds, and guides medicinal chemistry efforts more efficiently.

Predicting Side Effects of Lead Candidates

To further de-risk clinical development, we develop AI models that predict potential side effects of lead compounds before they enter human trials. These models support more informed candidate selection by identifying safety concerns early, thereby reducing the likelihood of adverse events during clinical studies.

Our models are trained on up-to-date post-marketing surveillance data from regulatory agencies such as the FDA. With deep expertise in handling large-scale adverse event datasets, our team builds predictive systems capable of predicting likely side effects for novel compounds. This enhances early safety profiling and improves clinical success rates.

Target-based drug repurposing

In target-based repurposing, AI models are used to predict novel biological targets for approved drugs—unlocking new therapeutic applications beyond their original indications.

By analyzing drug–target interactions across large molecular and bioactivity datasets, AI can identify off-target effects that may be beneficial in different disease contexts. These predictions help redirect existing compounds toward new indications, accelerating the development process and reducing the need for early-stage screening.

As illustrated, an approved drug known to act on a primary target can also be computationally linked to a novel target using an AI-based affinity prediction model. This insight can inform preclinical validation and clinical repositioning strategies—offering a cost-effective path to innovation.

Phenotype-based drug repurposing

In phenotype-based repurposing, approved drugs are identified for new indications by leveraging multi-omics data derived from patients or disease-relevant cell lines.

By analyzing transcriptomic, proteomic, epigenomic, and genomic profiles, AI models can match the molecular signatures of diseases with the known effects of existing drugs. This approach enables the identification of compounds that reverse or normalize disease-specific phenotypes at the systems level—without requiring prior knowledge of the exact molecular target.

As shown in the diagram, multi-omic measurements from disease models feed into AI algorithms that predict effective drug candidates, offering a powerful and unbiased strategy to uncover new therapeutic opportunities.

Disease-centric drug repurposing

In disease-centric drug repurposing, the process begins with a specific clinical indication and aims to identify approved drugs that can be repurposed for that indication.

The workflow starts by identifying disease-associated targets or biological pathways using curated databases such as Reactome and Open Targets. Next, candidate drugs that interact with these targets are retrieved from resources like ChEMBL, which provides bioactivity data on known compounds.

These drug candidates are then filtered and prioritized using additional databases and tools such as FDA and RDKit, evaluating them for safety profiles, regulatory status, and key physicochemical properties. The final output is a refined list of repurposed drug candidates with high therapeutic potential for the target indication.

Other Use Cases in AI And Software Development

The technologies and expertise behind our AI-driven drug discovery and repurposing solutions are also applicable to a broad range of other use cases.

Personalized Medicine

By analyzing multi-omics data from individual patients, we can develop AI models to predict drug responses and support the design of personalized treatment strategies. This enables precision medicine approaches tailored to the unique molecular profile of each patient.

Automated Data Mining from Biomedical Literature

We develop advanced language models that automatically curate pharmacological entities—including drugs, genes, diseases, and cell lines—by mining scientific publications in databases like PubMed, which hosts over 35 million articles.

Beyond simple extraction, our models can handle complex queries such as:

• Identifying all studies on drug repurposing for a given indication
• Extracting GWAS studies identifying novel targets for a given disease
• Retrieving literature on drug combinations for a given indication

These capabilities significantly accelerate knowledge extraction and hypothesis generation based on vast scientific literature.

Software Development for Bioinformatics

We provide cutting-edge software and data solutions tailored to the needs of bioinformatics, genomics, and medical research organizations. Our offerings include:

• Custom web platforms for data exploration with interactive visualizations
• Web applications integrated with machine learning models, enabling seamless data exploration, predictive analytics, and interactive decision-making
• Data scraping and data engineering pipelines for efficient acquisition of structured and unstructured content from both static and dynamic websites