AIDD/CADD - Chemistry Services - CRO Services - Viva Biotech

AIDD/CADD

Home / CRO Services / AIDD/CADD Empower the Entire Preclinical Drug Discovery Process

AIDD/CADD: Empowering End-to-End Drug Discovery

Target Selection

Target identification
Binding pocket prediction
Allosteric site prediction
Protein complex prediction
Membrane protein conformation prediction
Reverse target identification
Drug type evaluation

Hit Identification

De novo molecular design
Al-driven scaffold&functional group generation
Virtual screening
Covalent docking
Pharmacophore screening
Fragment growing/linking
Water network analysis

Hit-to-Lead

Protein-ligand interaction analysis (PLIF)
Al-driven scaffold optimization

Intelligent optimization of scaffold rigidity

Al-enhanced macrocyclization: GANs, VAEs, RL/DL
QSAR
Druggability evaluation

Lead Optimization

Free energy perturbation
ADMET prediction
Enhanced MD simulation
QM calculation for chemical reactions
Complex conformation analysis
Mechanism of action analysis
Scoring function optimization

Preclinical

ADMET optimization

LogD, pKa, wLogSol

Caco2, MDCK,PAMPA

BBB, PPB, Pgp, BCRP

Hum_LM, CYPinhibition

hERG, AMES, MNT, hepatotoxicity
Pharmacokinetic property optimization

Comprehensive
modality coverage
Al-driven
drug design
End-to-end modeling
capabilities
Dry-and wet-lab
integration
Robust computational
power

Integrated AIDD platform

Enhanced molecular dynamics

Antibody design platform

Customizable computational workflows
Al-driven molecular generation

High-accuracy FEP platform

Peptide design platform

High-throughput virtual screening based onproprietary Viva libraries
Big data-powered target analysis

ADMET prediction

PROTAC/MG computational solutions

Multimodal and high-dimensional data incorporation
Al-augmented virtual screening

Al-assisted enhanced toxicity profiling

RNA-targeting small molecule design

Al and wet-lab integration

Comprehensive Virtual Screening Solution Based on Multi-Strategy Integration

Customized and high-success-rate virtual screening solutions for efficient screening and accurate prediction

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Case

Leveraging core-hopping to efficiently increase the enrichment factor and hit rate for novel scaffolds within compound libraries.

For systems with flat-surface proteins, we identify allosteric pockets and locate hotspots within the protein's active and allosteric sites.

AI- and Proprietary-Model-Based ADMET Property Prediction

Prediction and optimization of ADMET and pharmacokinetic properties

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The ADMET Prediction Workflow

Data input

molecule structure or a.a. sequence
Feature generation

molecular descriptor, fingerprints, spatial charge map, etc.
Models training

AI model RF, SVM, GNN, etc.
ADMET predictions

LogP, logS, PSA, CYP inhibition, Caco2, PK/PD, etc.
Iterative property optimization

‌ADMET-optimized candidates

In-house NCA Fitting Server

Subject	b0	CMAX	CMAXD	TMAX	TLAG	CLST	CLSTP	TLST	LAMZHL	LAMZ	LAMZLL	LAMZUL
1	7.90557889985285	908.5932	908.5932	4	0	9.3486	9.71817401433272	24	2.95397187180416	0.234649214901495	4	24
2	7.58477681684899	787.2827	787.2827	4	0	8.1577	8.1617166958425	24	3.03273562647809	0.228555095441965	4	24
3	6.90409852511053	445.6481	445.6481	4	0	4.9184	4.81604358778232	24	0.222172740271905	0.222172740271905	4	24

Core Capabilities

ADMET prediction and optimization

Pharmacokinetic (PK) optimization

Proprietary Advantages

High Accuracy

Supports mechanistic studies

ADMET prediction for various types of biologics

Continuous model optimization based on proprietary data resources and state-of-the-art AI models

AIDD/CADD Platform

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Area of Interest

CRO Drug R&D Services

CDMO R&D and Production Services

EFS Investment＆Incubation Business

Media Cooperation

Investor Relations

Other

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