The Ainnocence platform currently offers two main engines;
This structure and rule-based design engine is used to generate novel compounds with desired R-groups and new cores for more optimized pharmacological properties and easier synthesis.
Lead Optimization Analog Expansion
This structure and rule-based design engine generates novel analogs of a desired scaffold for more optimized pharmacological properties. This enumeration opens endless possibilities for compound optimization, and multiple rounds lead to infinite combinations of novel analogs.
This design engine generates novel PROTAC degrader compounds based on the input structure-based parameters. These structures can be used by other modules for subsequent property prediction.
Target Binding And Selectivity Optimization
Protein Pocket Binding Affinity Prediction
The protein-pocket structure and ligand interaction is modeled for rapid prediction of the binding affinity between a protein and multiple screening compounds. This ultra-efficient virtual screening allows for screening of millions of compounds in a single day.
Protein Sequence Binding Affinity Prediction
All proteins consist of a primary sequence of 20 basic amino acids. This universal model of a protein sequence with a small molecule allows for an ultra-efficient virtual screening workflow for screening millions of compounds per day without the need of a protein structure.
Protein Pocket Detection
We provide an efficient and accurate platform to identify druggable and functionally relevant candidate pockets in protein structures
Compound Off-target Prediction
Avoiding off-target effects of a small molecule is a key hurdle to tackle for any drug discovery program. This engine helps predict the risk of unwanted binding affinity for different target families such as kinases, transporters, or receptors so cleaner small molecules can be further prioritized.
General Pharmacological Profile Optimization
The ADME (Absorption, Distribution, Metabolism, and Excretion) prediction module allows you to find the most optimized molecule using over 20 models covering each of the four domains to predict assay and chemical properties. This will give you an early conclusion regarding which compounds can be prioritized for screening.
Different toxicity domains such as cytotoxicity, carcinogenicity, and organ-injury can be predicted. This allows for prioritization of compounds for screening in different stages of drug-discovery, lowering the risk of a failed program due to undesirable toxicity properties.
Unique AI-based drug metabolism and pharmacokinetics (DMPK) platform can evaluate ADMET (distribution, metabolism, excretion, and toxicity) properties of compounds. The ability to quickly and accurately predict ADMET properties of compounds is beneficial to help you select molecular structures with a good drug-like properties to synthesize, minimizing the rate of attrition of drug candidates and increasing the efficiency of drug discovery overall.
Protein-Protein Binding Affinity Maturation
Therapeutic Antibody Modification
Our affinity maturation engine attempts to utilize AI techniques to perform in-silico therapeutic antibody maturations with better affinity improvement and least experiment cost.
Fusion Protein Engineering
We utilize sequence-based modeling approach to study and predict the binding affinity between fusion proteins and peptides.
Target-Antibody Binding Ranking Prediction
This module takes antibody/antigen sequences provided by the user and provide a ranking list for all antibody-antigen combinations in terms of their corresponding binding affinity values (AI predicted binding scores).
This model is designed to evaluate the humanness of antibody which potentially indicates the level of immunogenicity risk.
Humanization is a popular method to reduce the immunogenicity risk. This module is used to create human-like antibody with lower immunogenicity risk based on a novel algorithm
Protein Off-target Prediction
Protein Off-target Toxicity Prediction
Therapeutic antibodies/proteins are designed to have high specificity towards targets with least side effects that may be harmful to the human body. The Off-target engine helps to predict the risk of unwanted binding activity for large molecule drugs.