Traditional drug development is a costly and time-consuming process involving repetitive experiments on
millions of compounds. Bottlenecks often occur at every stage, from Hit Discovery to Lead Optimization,
particularly for novel targets where low predictability makes them high-risk. SM-ARS® eliminates these
bottlenecks by delivering fast, high-precision candidate generation to maximize research efficiency.
Using AI-driven virtual screening and structural analysis, it identifies promising candidates while eliminating high-risk structures early, enabling researchers to advance with fewer experiments. Our precision design process is supported by the ABSC high-performance computing environment. This robust infrastructure ensures that even as the scale of exploration expands, your research workflow remains stable, efficient, and uninterrupted. At its core is Syntekabio’s proprietary DEEPMATCHER® technology—an AI model combining virtual screening with 3D structure-based binding prediction to evaluate binding potential and stability at the molecular level.
AI-driven screening of 10 billion+ compounds using LM-VS™ for rapid, automated lead identification Our proprietary LLM-based engine, automates the early-stage selection process, surfacing research-grade candidates with unprecedented speed.
High-precision evaluation of binding affinity and stability via DEEPMATCHER®'s 3D structural simulations. Evaluate target-ligand interactions with DEEPMATCHER®. Our 3D docking protocols integrate binding pose prediction and structural scoring to determine binding affinity, ensuring robust lead selection.
Early-stage failure prevention through comprehensive profiling of ADMET, Drug-likeness, solubility, and toxicity. This rigorous pre-assessment optimizes the lead generation process by filtering out sub-optimal compounds before they reach the next phase.
Final lead candidates are prioritized based on a multi-dimensional assessment of binding profiles, molecular properties, and scaffold diversity. We provide actionable data designed to support critical decision-making for PoC validation and pipeline advancement.
For de novo targets, we engineer novel molecules from scratch based on publicly available protein structures and peer-reviewed literature. By integrating these foundational insights, we perform precise de novo design and advanced simulations to accurately predict binding affinity and interaction stability.
The LM-VS™ evaluates binding potential at scale, utilizing sepcific scoring algorithms to filter for candidates with the high probability of synthetic success.
DEEPMATCHER® utilize advanced 3D simulations and Convolutional Neural Networks (CNN) to predict binding affinity and molecular stability. Our flexible docking simulate real-world molecular dynamics to derive optimized binding poses and superior lead characteristics.
The report provide refined candidate lists supported by aggregated metrics, ensuring all results are formatted for immediate experimental validation and downstream pipeline progression.
Reducing hit discovery timelines from months to within three weeks through AI-driven automation
Minimizing unnecessary experiments and candidate exploration costs through in silico analysis in the preclinical stage
Early validation of binding stability and target suitability through AI-based structural prediction and MD simulations
Expanding applicability and pipeline flexibility through AI predictive models suitable for rare and novel targets
These figures are based on analysis data from internal Syntekabio projects and proof-of-concept (PoC) studies conducted between 2021 and 2025.
SM-ARS®
AI-driven candidate discovery enables rapid PoC validation without extensive upfront investment in biopharmaceuticals.
Rapidly derive candidates even for targets with limited or no structural information through AI-driven de novo design.
AI-driven exploration enables the discovery of diverse structural scaffolds with varying mechanisms of action to specific targets.
Rapidly discover new chemicals by screening targets or indications, accelerating pipeline expansion for new Indication development.
Pre-selected pipeline compounds are delivered as packaged assets through large-scale AI analysis, enabling rapid evaluation and immediate research integration.
Candidate Targets: BCL2, BTK, CDK2/4/6, FGFR1/2/3, KRAS, and others.
Exclusive, time-bound access to an AI-driven small-molecule discovery platform, integrating large-scale virtual screening, high-performance computing, and dedicated scientific support—operated as an extension of in-house R&D.
This service provides customized drug candidate discovery based on customer targets, supported by AI-driven analysis and flexible milestone-based pricing.
