Explore our cutting-edge AI-driven drug design services, tailored to meet your R&D requirements across various modalities. Experience rapid results with a turnaround time of just 1 week for computational outputs, and a comprehensive delivery – including both computational analyses, molecule production and wet lab testing – in just 1 month.

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).

Protein Humanization

Antibody Humanness

This model is designed to evaluate the humanness of antibody which potentially indicates the level of immunogenicity risk.

Antibody Humanization

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 evaluation

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.

Molecule Generation

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.

Toxicity Prediction

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.


Therapeutic RNA encompasses a wide range of RNA molecules that can either treat or prevent diseases. The design and modification of these molecules is a complex process requiring knowledge in molecular biology, computational biology, and biophysics. The optimization of RNA design increases the efficacy and reduces potential side effects.

  • Flexible Selection of Constraints: This involves selecting specific parameters for the optimization process. By understanding the intended application and organism in which the mRNA will be used, specific codon constraints can be set to ensure maximum efficiency and functionality.

  • Multiple Optimization Strategies Such as Prioritizing Expression Or Maintaining Low Abundance Passwords: Different strategies can be adopted based on the end goal. For instance, if the aim is high protein expression, codons that are translated more efficiently in the host organism can be prioritized. Conversely, for security or proprietary concerns, certain “password” sequences of low abundance can be incorporated.

  • Introduction, Mutation, and Elimination of UTR Components: The Untranslated Regions (UTRs) play pivotal roles in regulating mRNA stability, localization, and translation. By introducing specific motifs or mutating existing ones, the behavior of the mRNA molecule can be tailored to the desired outcome. Conversely, eliminating certain UTR components can reduce unwanted side-effects or interactions.

  • Design High Expression UTR from Scratch: A UTR designed for high expression would contain elements that enhance translation and mRNA stability. By combining known enhancer sequences and avoiding inhibitory motifs, one can craft UTRs that drive robust protein production.

  • Inhibition Efficiency Evaluation: Small interfering RNAs (siRNAs) function by targeting specific mRNA molecules for degradation, thus inhibiting their translation. Evaluating their efficiency involves checking the degree to which the target mRNA is degraded and ensuring minimal off-target effects.

  • Specific Evaluation: Beyond just efficacy, it’s important to ensure that the siRNA is specific to its target. This means checking against the entire transcriptome to ensure that other mRNAs are not inadvertently targeted.

  • Modified Base Prediction: Bases in RNA can be chemically modified to enhance their stability, reduce immunogenicity, or improve their functionality. Predicting which modifications would be beneficial involves a mix of computational tools and experimental validation.

  • Design of Subsidiary Units Such as Cap, polyA, and Hanging Bases: These units play crucial roles in the stability, localization, and translation of the RNA molecule. For instance, the 5′ cap protects mRNA from degradation, the polyA tail aids in nuclear export and stability, and certain ‘hanging’ or overhanging bases can aid in the efficiency of siRNA incorporation into the RISC complex.