Features
What KumiChem can help?
ASO design
The platform automates target site identification by retrieving full transcript sequences, recognizing and annotating splice sites, and screening secondary structures to locate accessible single-stranded regions suitable for binding.
It generates large batches of ASO candidates at scale while proposing optimized chemical modifications and backbone chemistries to enhance stability, potency, and delivery. Integrated knowledge modules connect each design to the patent landscape for freedom-to-operate assessment and to the latest clinical and scientific evidence. Candidates are then evaluated through multi-parameter filtering and ranking, enabling intelligent prioritization of the most promising ASOs for experimental validation.
siRNA Design
The platform streamlines siRNA development by automatically identifying optimal target sites from full mRNA sequences using isoform-aware and structure-guided accessibility analysis.
It performs high-throughput generation of siRNA duplexes with built-in thermodynamic balancing and chemistry optimization to enhance potency and stability.
Integrated intelligence modules evaluate off-target risk, seed homology, and immunogenicity while linking each design to relevant patents and scientific literature. All candidates are then subjected to multi-parameter filtering and ranking, enabling efficient prioritization of the most promising 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 agents
- 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