An intelligent AI platform
that supports your daily drug discovery tasks
From early concepts to viable drug candidates —
Empowering the journey toward greater challenges
How it works
The KumiChem agentic infrastructure
★ Prioritized RNA Candidates
Target-aware · Optimized · Safety-ranked
KUMICHEM
Agentic Intelligence
for RNA Drug Discovery
Data Foundation
Public · Proprietary · Partner data
Curate · Connect
Specialized Models
ML · Transfer learning · Foundation models
Learn · Predict
Data Foundation
Public · Proprietary · Partner data
Curate · Connect
Specialized Models
ML · Transfer learning · Foundation models
Learn · Predict
KUMICHEM
Agentic Intelligence
for RNA Drug Discovery
★ Prioritized RNA Candidates
Target-aware · Optimized · Safety-ranked
Why KumiChem
AI-enabled design vs. the manual process
The same discovery journey, radically compressed
100K+
Toxicity & modification datapoints
All-in-One
ASO, siRNA, data & enterprise tools, unified
Up to 200x
Faster in silico than manual design
Features
AI modules purpose-built for RNA drug discovery
ASO design
KumiChem takes each target from sequence to lead: retrieving the full transcript, mapping splice sites and secondary structure to locate accessible binding sites, then generating large batches of chemically optimized ASO candidates at scale.
Every candidate is screened with in silico toxicity and safety evaluation, and cross-referenced against the patent landscape and latest clinical evidence. Multi-parameter filtering and ranking then prioritize the most promising, developable ASOs for experimental validation.
siRNA Design
KumiChem streamlines siRNA development with off-target-aware sequence design, identifying optimal target sites from full mRNA sequences using isoform-aware, structure-guided accessibility analysis.
It performs high-throughput generation of siRNA duplexes with built-in thermodynamic balancing and chemical modification design to enhance potency and stability.
Integrated models score off-target risk, seed homology, and immunogenicity for every candidate, linking each design to relevant patents and scientific literature. Multi-parameter filtering and ranking then prioritize the most selective, developable siRNAs for experimental validation.
Data curation
The MetaAgent achieves effectively unlimited memory by batch-processing large collections of records and synthesizing structured summaries, allowing continuous awareness of all ingested content without context-window constraints.
For example, it can extract mechanism–disease–chemical relationships from the CTD database, mine modification patterns and delivery chemistries from patent corpora, and normalize experimental results from internal screening reports into unified knowledge graphs.
This curated foundation enables downstream agents to reason across heterogeneous sources, linking literature evidence, proprietary datasets, and regulatory knowledge to support more informed new drug design decisions.
Accelerate real-world drug discovery
Validated AI agents, scalable workflows, and enterprise-grade infrastructure for modern pharmaceutical research
Most popular
Enterprise (Standard)
For companies with small- to medium-scale workloads seeking to experiment with AIDD tooling powered by an AI agent.
- 2 users
- 40 agent runs/user/day
- 300 GB storage
- All RNA workflows
- Data curation suite
- Cloud deployment
Special Offer: 1 FREE custom workflow ($50,000 Value)
Enterprise (Pro)
Designed for companies aiming to strengthen and scale their capabilities with a comprehensive AI agent package to support long-term milestones.
- 3 team users
- 60 agent runs/user/day
- 2 TB storage/user
- All RNA workflows
- Data curation suite
- Custom templates
- On-premise option
- Enterprise support
- Early feature access
The cost is calculated individually depending on the needs
Domain voices
User-friendly AI platform
Traditional drug development is typically a laborious and costly process with unavoidable ethical concerns regarding animal experiments and human trials. Employing AI can circumvent these issues and increase the efficiency of developing more effective and affordable drugs.
It is exciting to see that DLeader is providing this kind of user-friendly AI platform that enables researchers and companies to boost biomedical research and pharmaceutical development, ultimately benefiting educational purposes and public health.
W. Z, PostDoc fellow in Life Science, Japan
Recognition of validity
The premise for conducting data analysis and using its results as guidelines or bases is the recognition of its validity. Validity primarily refers to "statistical significance," with criteria rooted in probability theory's Law of Large Numbers and Central Limit Theorem.
Additionally, data science's dimensionality reduction, forecasting, and classification clustering methods may also utilize highly advanced modern mathematical tools and cutting-edge theories, which contribute to the thriving field of data science and provide a solid scientific foundation for the platform.
Y. L, PhD candidate in Math, Japan
DLeader's commitment to innovative drug development
Recent research in AI and data mining has significantly accelerated the development of technologies across various domains.
We're excited to see DLeader's commitment to innovative drug development, and we anticipate it becoming a pioneering leader in this field
Y. J, PhD candidate in MultiMedia, Japan
Revolutionize the industry
Developing a new platform for AI-based drug discovery could revolutionize the industry and lead to faster, more effective treatments.
I'm truly impressed by your courage to tackle such a challenging subject as a startup. I can't wait to see the impact your work will have on the healthcare industry.
Kosagi, MNC Pharma Company, Japan
The potential to significantly enhance patient
The integration of AI into medical research and clinical practice has the potential to significantly enhance patient care by providing faster, more accurate diagnoses and personalized treatment plans. Leveraging AI can optimize the use of medical data, leading to groundbreaking advancements.
We are inspired by Dleader's dedication to bringing these innovations into the healthcare field. Looking forward to seeing how your efforts will improve public health.
A. H. PostDoc fellow in Public Health, Pecking University