PozeSCAF is a preclinical drug discovery CRO, that leverages an in-house generative AI-based discovery platform – AxDrug integrated with computational chemistry tools and synthetic chemistry lab to create best-in-class lead candidates for our clients.
AxDrug Platform – AI & Computational Powered Drug Discovery
DISCOVERY CRO SERVICES
HIT IDENTIFICATION
IN SILICO & SYNTHESIS
State-of-the-Art AI and Computational Chemistry Services for Swift and Precise Virtual Screening of Extensive Databases with Exceptional Hit Rates.
HIT TO LEAD GENERATION
IN SILICO & SYNTHESIS
Leverage Advanced Generative AI and Computational Biophysics for Scaffold Hopping, Enumeration, and Precise Binding Affinity Prediction Based on Quantum Mechanics. Uncover Lead Molecules from Existing Hits with Unprecedented Precision.
LEAD OPTIMIZATION
IN SILICO & SYNTHESIS
Multi-parameter optimization of lead molecules leveraging advanced Generative AI protocols coupled with computational simulations & Free energy perturbations to identify a potential drug candidate.
DISCOVERY SYNTHESIS
MEDCHEM & SYNTHESIS
Medicinal Chemistry Optimization and Synthesis of Small molecules, Peptides, PROTACs, Macromolecules, Carbohydrates, etc., for various Therapeutic Areas. Our synthesis Capability spans from Milligrams to Kilograms.
THE ADVANTAGE
The PozeSCAF team understands the system at an atomic level, discerning its pattern of interactions through computational methods and converting them into properties. These properties can then be explored using machine learning and/or deep learning tools, thus transforming them into knowledge and hypotheses.
Ideation, automation, prediction, optimization, toxicity, and attrition are the key factors that control Drug Discovery and drug Development. They can be comprehensively addressed with the derived knowledge and hypotheses. Thus, PozeSCAF automates the Drug Discovery, Drug Development, and Deployment Map using AI and computational chemistry methods (Computer-Aided Drug Design Models (CADD) / MoMo).
Our Drug Discovery team possesses strong expertise in computational chemistry and AI. The major advantage lies in having an experienced R&D team with extensive experience in drug discovery using computational chemistry. We can understand the problem by leveraging advanced technologies of computational chemistry and AI for innovative solutions with a high success rate in less time, thus reducing research and development (R&D) costs for our clients.
OUR ARMOURY
Expansive Chemical Space
Leveraging our 20 billion database of drug-like small molecules, generated through a fusion of generative AI/chemistry and commercial libraries, as the foundational step for screening protocols.
Generative AI
Generating potent, selective, and drug-like compounds using graph-based transformers within a Reinforcement Learning framework.
Machine Learning & Deep Learning Models
Our continually evolving deep learning and machine learning framework is designed to precisely identify drug candidates from our extensive chemical space, thereby accelerating the discovery process.
Modeling Proteins and Validating Targets
Enhanced AI Protocols for Accurate Prediction of Protein Structure, covering Highly Disordered Proteins, along with the identification of druggable sites on the targets.
Comprehensive Virtual Screening Protocols
Rapid Virtual Screening of Extensive Databases through Consensus Models: Active Learning Docking with Soft Potentials and Accurate Docking via Quantum Mechanics.
Molecular Dynamic Simulations & FEP Studies
Proven Molecular Dynamic Simulations with Enhanced Protocols and Free Energy Perturbation Studies for Evaluating Binding Affinity and Prioritizing Potential Candidates.
ADME and Toxicity Prediction Models
Employ the capabilities of our sophisticated and highly predictive ADME Models to identify drug-like compounds within expansive libraries with precision and efficiency.
Off-Target Prediction and Polypharmacology
Our foundational biological models identify off-target effects, assess selectivity, and evaluate polypharmacology to uncover multi-target drug candidates.
Medicinal chemistry
Our proficient medicinal chemistry team collaborates seamlessly with the synthetic chemistry team, working exclusively to create focused libraries and structure-activity relationship (SAR) insights. This synergy optimizes the synthetic routes for the desired compounds.
DISCOVERY CRO WORKFLOW
CRO Mastery: Tailored Solutions for Seamless Success
FEW OF OUR CLIENTS
Our Recent Publications
- First Total Synthesis of (±)-Anthogorgiene A, Marine-Derived Guaiazulene-Based Secondary Metabolite
- A Concise Formal Total Synthesis of (±)-Laurokamurene B and (±)-β-Cuparenone
- Identification of SARS-CoV-2 Main Protease Inhibitors Using Structure Based Virtual Screening and Molecular Dynamics Simulation of DrugBank Database
- In silico analysis and identification of promising hits against 2019 novel coronavirus 3C-like main protease enzyme