Variant Discovery & Drug Target ID
Overview
In the contemporary landscape of healthcare, our sequencing technology and analysis provide valuable insights into our unique physiological characteristics. In this field, variant discovery and drug target identification have become key research areas, enabling us to deeply study the relationship between complex genes and health from different perspectives, analyze the secrets in genes through artificial intelligence, and discover therapeutic targets for diseases more quickly and accurately.
Variation discovery is the identification of variations in the genome caused by single-nucleotide polymorphisms (SNPS), insertions, deletions, etc. These variations occur naturally among the population. These variations can affect an individual's likelihood of falling ill, their response to environmental factors, and the therapeutic effect of drugs. On the other hand, drug target identification involves determining specific molecules or pathways in the body that can be targeted by drugs to achieve therapeutic effects. By combining artificial intelligence with genomic data, we can accelerate the discovery of new drug targets that are both effective and safe.
Our Variant Discovery & Drug Target ID Service Elevates Your Research with:
- Accelerate drug target identification: By using deep learning models trained on large genomic datasets, we can quickly screen out potential drug targets. This not only shortens the time for drug discovery, but also increases the high therapeutic potential of the screened targets.
- Insights into Disease Mechanisms: Our approach uncovers novel genetic variants and drug targets that shed light on the underlying mechanisms of complex diseases. This knowledge is crucial for developing targeted therapies and improving patient outcomes.
- Research Acceleration Tool: The integration of AI, deep learning, and genomics in our service acts as a powerful catalyst for research, facilitating the rapid discovery of new genes, pathways, and therapeutic targets involved in disease pathogenesis.
What Is Variant Discovery & Drug Target ID
Variation discovery and drug target identification are two interwoven research directions in modern genomic medicine. The focus of variation discovery is to identify genetic variations, such as changes in individual nucleotides or structural alterations. These variations in a person's genome can reveal disease susceptibility and drug efficacy. On the other hand, drug target identification involves gene mapping, aiming at specific molecular or biological pathways that drugs can regulate to treat diseases. They jointly utilize genomic data to accelerate the discovery of new therapeutic targets and enhance efficacy and safety.
How We Deliver This Solution
Sequencing
Whole Genome Sequencing (WGS)
- Purpose: Comprehensive detection of single-nucleotide variants (SNVs), structural variants (SVs), and copy number alterations (CNAs) across populations.
- Application: Identifies rare disease-causing mutations, population-specific genetic markers, and drug-response biomarkers.
- Link: /pop-genomics/seq/whole-genome-reseq.html
Whole Exome Sequencing (WES)
- What it does: Sequences the protein-coding regions of the genome at a large scale to pinpoint functional genetic variants.
- How it helps: Accelerates the discovery of genes linked to diseases and identifies potential new therapeutic targets in large study groups.
- Link: /pop-genomics/seq/whole-exome-sequencing.html
Genotyping Arrays
- What it does: Provides a cost-effective, high-throughput method to genotype pre-selected SNPs. This data is optimized for AI-driven calculations, such as Polygenic Risk Scores (PRS) and therapy target prioritization.
- How it helps: Enables the scalable deployment of PRS and drug target identification in healthcare systems or clinical trials, using deep learning to improve prediction accuracy across large populations.
- Link: /pop-genomics/seq/gbs.html
Bioinformatics
PRS Algorithm Development
- Purpose: Integrates multi-ancestry GWAS data to build robust, generalizable risk scores.
- Application: Gene mutations vary among different populations. Reducing population-specific bias through algorithms can ensure accurate research on gene mutation targets.
Population Stratification Correction
- Purpose: Adjusts for ancestry-specific genetic effects to minimize bias in PRS accuracy.
- Application: Optimizes personalized dosing regimens for high-risk individuals and identifies novel drug targets by predicting how genetic variations influence drug metabolism and efficacy.
Pharmacokinetic/Pharmacodynamic (PK/PD) Modeling
- Purpose: Links PRS-derived genetic risk to drug exposure-response relationships.
- Application: The process of computer prediction of gene variations and screening of targets can accelerate the research and development of targeted drugs and promote the launch of new drugs.
Service flowchart
Figure 1: How We Deliver This Solution: AI-Enhanced Therapeutic Target Discovery Workflow
Our Advantages
Advanced AI-Driven Variant Interpretation
Our platform uses sophisticated AI to analyze genetic variations accurately. By training deep learning models on large genomic datasets, the system can tell harmless variations from disease-related ones, predict how they might function, and evaluate their role in disease risk or drug response.
Comprehensive Pharmacogenomic Integration & PRS Customization
We integrate key information such as variations in drug-metabolizing enzymes and combine it with multi-gene risk scoring models designed for specific diseases or drug responses to form personalized assessment plans. This integrated model can accurately predict the therapeutic effects and adverse reaction risks of different populations. With the help of multi-ethnic genomic data, the PRS model has wide applicability and supports precise risk stratification and individualized treatment planning. This systematic strategy not only optimizes clinical efficacy but also promotes the development of new treatment strategies by analyzing the genetic factors related to drug responses.
High-Throughput Variant Screening & Prioritization
Using high-throughput sequencing and AI-based bioinformatics tools, our platform can process huge amounts of genetic data to quickly pinpoint variations that may have clinical importance—including rare mutations and larger structural changes. By ranking variants based on their likely impact and disease links, we make the drug target discovery process faster and more efficient.
Scalable PRS Validation & Clinical Translation
We've thoroughly tested our polygenic risk score (PRS) model using real-world data from over 100,000 patients. This ensures the model works reliably across different populations. Our platform supports a complete workflow—from variant screening and risk scoring to clinical decision-making—helping to speed up regulatory approval and practical use in areas like cancer, heart disease, and rare disorders. By closing the gap between genomic research and patient care, we help hospitals deliver more personalized and effective treatments.
Applications
Beyond Conventional Variant Detection: AI-Driven Rare Variant Identification
Traditional methods for discovering variations usually focus more on common genetic variations, which may thus miss those rare but functionally significant variations. To this end, we have introduced a service based on deep learning algorithms, which have been trained on diverse genomic datasets and can effectively identify and prioritize the screening of rare variations that may be related to treatment. By integrating whole-genome sequencing data and advanced bioinformatics analysis processes, we can identify mutations with lower frequencies that may cause complex diseases or affect drug responses. This strategy is conducive to the discovery of new drug targets and biomarkers, especially applicable to heterogeneous diseases such as cancer - in which rare variations often drive tumor progression or lead to treatment resistance.
Dynamic Drug Response Prediction: AI-Enhanced Pharmacogenomic Mapping
People's responses to drugs can vary significantly due to their genetic makeup—impacting how well a treatment works and what side effects may occur. Our service combines pharmacogenomic data with AI-powered models to predict an individual's likely response to a drug based on their DNA. We analyze variations in genes involved in drug metabolism, transport, and targeting to build a personalized response profile. This not only helps doctors choose the right drug and dosage for each patient, but can also reveal new drug targets by uncovering genetic factors that influence treatment outcomes. For instance, in cancer care, this method may help identify targets to overcome chemotherapy resistance in certain patient groups.
Cross-Population Insights: Multi-Ethnic Genomic Analysis for Global Therapeutic Discovery
Genetic differences among populations play a key role in disease risk and drug response, yet many genetic studies have focused on limited or homogeneous groups. By including data from underrepresented communities, we make sure our findings are equitable and broadly useful. As a result, we can improve the accuracy of risk scores and drug response predictions across different populations—while also uncovering novel treatment targets that may be specific to certain ethnic or regional groups, supporting the development of therapies that work for people worldwide.
Demo
Figure 2: Representative structures for each TRP channel subfamily, coloured to highlight common structural features. (Koivisto, 2022)
Case Study
Decoding triancestral origins, archaic introgression, and natural selection in the Japanese population by whole-genome sequencing.
Journal：Sci Adv.
Published：2024
Adeno-associated virus (AAV) has emerged as a leading vector for gene therapy, offering promising treatment options for various diseases, including hepatocellular carcinoma, X-linked cone dystrophy, hemoglobinopathies, and Alzheimer's disease. Despite the approval of several AAV-based therapies by the FDA, significant barriers impede the broader application of AAV vectors. These challenges include poor vector biodistribution, reduced target efficiency, limited transduction safety, and potential immune responses. These issues highlight the need for optimizing AAV delivery systems to enhance transduction efficiency, target potency, and selective expression, thereby overcoming current limitations in gene therapy.
This review presents several optimized strategies to improve AAV-based gene delivery. These include redesigning the virus shell (capsid) to improve tissue targeting and avoid immune detection, increasing the amount of genetic material the virus can carry, and reducing immune reactions through smarter vector design or temporary immunosuppression. The review also highlights how artificial intelligence (AI) can speed up the design of improved AAV vectors—using predictive modeling and high-throughput screening to understand structure-function relationships and protein interactions. AI helps scientists precisely modify the capsid and genome of AAV, leading to safer and more efficient gene therapies. Examples from preclinical and clinical studies show AAV's potential in treating cancer, neurodegenerative conditions, and eye diseases, as well as its emerging role in developing vaccines for infectious diseases such as COVID-19 and monkeypox.
Researchers have achieved better targeting, higher gene transfer efficiency, and improved safety by combining optimized AAV delivery strategies with AI-assisted design. Capsid engineering has successfully modified AAV tropism, enabling tissue-specific gene delivery and reducing off-target effects. Efforts to increase packaging capacity have expanded the therapeutic potential of AAV vectors, allowing for the treatment of diseases requiring larger genetic constructs. Furthermore, strategies to mitigate immune responses have enhanced the safety and tolerability of AAV-based therapies. The review highlights the successful application of AAV in multiple preclinical and clinical studies, with several FDA-approved therapies already impacting patient care. Additionally, the exploration of AAV in emerging infectious diseases, such as SARS-CoV-2 and monkeypox, underscores its versatility and potential as a future therapeutic platform. These advances help integrate AAV into precision medicine, opening new treatment possibilities for a wide spectrum of diseases.
Figure 3: AAV‐based COVID‐19 vaccines target SARS‐CoV2 S protein RBD. The figure illustrates key functional domains of the SARS‐CoV‐2 spike protein, including the receptor binding domain (RBD) that interacts with human ACE2 receptors and the N‐terminal domain (NTD).
FAQ
Deep learning algorithms are good at detecting complex patterns in large-scale genomic data. For variation discovery, genomic variations can be analyzed more precisely. In drug target identification, deep learning predicts the functional impact of variations (for example, on protein structure or gene regulation) and prioritizes targets based on their therapeutic relevance. This will lead to more precise and actionable insights than traditional methods.
Absolutely. Rare diseases often have a strong genetic basis, making variant discovery critical for identifying causal mutations. AI models trained on diverse genomic datasets can detect rare variants linked to rare diseases and predict their pathogenicity. This enables the discovery of novel targets for conditions with limited treatment options, driving the development of orphan drugs.
References
- Koivisto AP, Belvisi MG, Gaudet R, et al. Advances in TRP channel drug discovery: from target validation to clinical studies. Nat Rev Drug Discov. 2022 Jan;21(1):41-59. doi: 10.1038/s41573-021-00268-4.
- Yin L, He H, Zhang H, et al. Revolution of AAV in Drug Discovery: From Delivery System to Clinical Application. J Med Virol. 2025 Jun;97(6):e70447. doi: 10.1002/jmv.70447.
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