Medicilon AI Drug Discovery Service Platform: An Efficient Booster for New Drug Development

In recent years, the AI pharmaceutical industry has been entering a prosperous golden period of development. With the rapid advancement of AI technology and the widespread application of big data, AI has shown tremendous potential and value in the field of drug development. The traditional drug development process is lengthy, costly, and has a high failure rate. The introduction of AI technology has made drug development more efficient and precise.

Medicilon, as a one-stop biopharmaceutical preclinical CRO, has been actively exploring cutting-edge technologies in drug development for twenty years. In the AI pharmaceutical field, Medicilon has established an AI drug discovery platform based on collaborations with several AI innovative drug development companies such as InSilico Medicine and MindRink.

Services of Medicilon AI Drug Discovery Service Platform.webp

Medicilon's AI technology service platform can provide protein structure prediction and simulation, binding site discovery, information extraction and cleansing, as well as customized project database construction, meeting diverse needs of researchers. In addition, the platform deeply supports key research and development stages such as Target-to-hit, Hit-to-lead, Lead-to-PCC, providing comprehensive technical support for drug development and accelerating the drug development pipeline process.

01 AI + Structural Biology

Medicilon has extensive research experience in structural biology, providing strong service support for Structure-Based Drug Design (SBDD).

Protein Expression And Purification Services

Multiple protein expression systems

Various purification tool enzymes

Years of experience in expression and purification

Protein Crystallization Services

High-throughput screening for crystallization conditions

Customized crystal cultivation

Collaboration with multiple partners

AI Protein Structure Prediction

Application of open-source data models

Collaboration with multiple partners to provide more accurate predictions

02 AI+Target-to-HIT

Medicilon's AI Target Bioanalysis utilizes methods such as biological pathway analysis, omics analysis, and organ expression analysis to deeply explore the connections of biomarkers, uncovering potential drug targets. In addition, the platform collaborates closely with multiple virtual compound library suppliers to conduct efficient virtual screening, rapidly generating hit compounds, and verifying them through various technological methods.

Medicilon AI Target Bioanalysis

Biological Pathway Analysis
AI-assisted construction of biological pathway databases
AI virtual analysis of biological pathway signal topology

Omics Analysis
Machine learning for exploring biomarker connections

Organ Expression Analysis
Discovery of expression sites by integrating big data with reality

Medicilon AI Target Drug Molecule Screening

(1) Deep collaboration with multiple virtual compound library suppliers (Enamine, Life chemicals)
(2) Efficient virtual screening
(3) Support for high-throughput screening methods such as TR-FRET, FP
(4) Rapid generation and derivatization of hit compounds
(5) Support for Hit validation using NMR, MST, SPR

AI+ Hit-to-Lead

From Hit to Lead compounds, Medicilon AI Drug Discovery Platform utilizes advanced AIDD/SBDD technologies to design novel scaffold molecules and optimize the structure and activity of drug molecules.

(1) AIDD/SBDD
(2) Novel scaffolds
(3) Molecular docking
(4) Molecular dynamics simulations
(5) Free energy perturbation
(6) Synthetic feasibility analysis

AI+ Lead Compound Optimization

In the journey of drug development, optimizing lead compounds is a crucial step. Medicilon AI Drug Discovery Platform, leveraging its cutting-edge technology and extensive industry experience, provides comprehensive and precise support for lead compound optimization.

(1) AI molecular generation models
(2) AI activity prediction
(3) Structure-activity relationship (SAR) studies
(4) ADME prediction
(5) Physicochemical property prediction
(6) Selectivity/off-target activity

AI+ Drug Candidates

Regarding candidate compounds, Medicilon AI Drug Discovery Platform's services cover multiple critical stages including activity testing, animal efficacy validation, ensuring comprehensive validation and evaluation of candidate drugs before clinical application.

(1) Activity testing
(2) Animal efficacy validation
(3) Pharmacokinetic studies
(4) Toxicology studies

Medicilon AI Drug Discovery Platform - AIDD Case Studies

What is preclinical testing?

In the process of preclinical testing of a compound or biological agent into a drug, the compound involved must go through the testing phase. First, we need to identify potential targets that can treat the disease. Then, a variety of compounds or preparations are screened out. Any compound that has shown potential as a drug for the treatment of this disease needs to be tested for toxicity before clinical testing to reduce the possibility of injury. preclinical testing What is the basis of preclinical testing? According to US Food and Drug Administration (FDA) regulations, a series of tests are required before a new drug is approved for use. In the first stage, basic research determines a hypothetical target for the treatment of a certain disease, and then screens small molecules or biological compounds to discover any substance with the potential to treat the disease. Then, a preclinical research phase followed, before which, as described above, the potential toxicity of the compou

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Inventory of the three major in vitro pharmacokinetic research methods

The metabolic properties of a compound are an essential factor in whether or not it can be used as a drug in the clinical setting, so pharmacokinetic studies of newly synthesized compounds are required in drug development. In vitro incubation with liver microsomes, recombinant CYP450 enzyme lines, and in vitro incubation with hepatocytes are some of the more common in vitro drug metabolism methods. 1. In vitro incubation method with liver microsomes The metabolic stability and metabolic phenotypes of candidate compounds in different species of liver microsomes are good predictors of the metabolic properties of compounds in vivo. They are practical tools for evaluating candidate compounds in the pre-development phase of drug development. Liver microsomes include rat liver microsomes, human liver microsomes, canine liver microsomes, monkey liver microsomes, and mouse liver microsomes. In in vitro incubation of the liver, microsomes are the "gold standard" for in vitro d

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Novel Parkinson’s Therapies Possible with New Mouse Model

Parkinson's disease (PD) is a neurodegenerative disorder that is marked by the accumulation of the protein, α-synuclein (αS), into clumps known as Lewy bodies, which diminish neural health. Now, researchers from Brigham and Women's Hospital (BWH) report the development of a mouse model to induce PD-like αS aggregation, leading to resting tremor and abnormal movement control. The mouse responds to L-DOPA, similarly to patients with PD. The team's study (“Abrogating Native α-Synuclein Tetramers in Mice Causes a L-DOPA-Responsive Motor Syndrome Closely Resembling Parkinson’s Disease”) on the use of this transgenic mouse model appears in Neuron . “α-Synuclein (αS) regulates vesicle exocytosis but forms insoluble deposits in PD. Developing disease-modifying therapies requires animal models that reproduce cardinal features of PD. We recently described a previously unrecognized physiological form of αS, α-helical tetramers, and showed that familial PD-causing missense mutati

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