Global Certificate Course in Computational ADME Modeling

Monday, 09 February 2026 11:41:30

International applicants and their qualifications are accepted

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Overview

Overview

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Global Certificate Course in Computational ADME Modeling provides in-depth training in predicting drug absorption, distribution, metabolism, and excretion (ADME).


This course is ideal for pharmacologists, chemists, and bioinformaticians. Learn advanced computational techniques and software applications for ADME prediction.


Master quantitative structure-activity relationship (QSAR) modeling and physiologically based pharmacokinetic (PBPK) modeling. The Computational ADME Modeling certificate enhances career prospects in drug discovery and development.


Gain valuable skills in data analysis and predictive modeling. Enroll now and advance your expertise in Computational ADME Modeling!

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Computational ADME Modeling: Master the art of predicting drug absorption, distribution, metabolism, and excretion (ADME) using cutting-edge computational techniques. This global certificate course provides hands-on training in advanced software and methodologies, boosting your expertise in pharmacokinetics (PK) and pharmacodynamics (PD). Gain a competitive edge in the pharmaceutical industry with enhanced career prospects in drug discovery and development. Our unique curriculum includes real-world case studies and industry expert mentorship, setting you apart from the competition. Unlock your potential in this rapidly growing field with our comprehensive Computational ADME Modeling program.

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Entry requirements

The program operates on an open enrollment basis, and there are no specific entry requirements. Individuals with a genuine interest in the subject matter are welcome to participate.

International applicants and their qualifications are accepted.

Step into a transformative journey at LSIB, where you'll become part of a vibrant community of students from over 157 nationalities.

At LSIB, we are a global family. When you join us, your qualifications are recognized and accepted, making you a valued member of our diverse, internationally connected community.

Course Content

• Introduction to ADME and its Importance in Drug Discovery
• Physiologically Based Pharmacokinetic (PBPK) Modeling: Principles and Applications
• Absorption Modeling: In vitro-In vivo Correlation (IVIVC) and Permeability Prediction
• Metabolism Modeling: Enzyme Kinetics and Cytochrome P450 Inhibition
• Computational ADME Prediction using Machine Learning
• Excretion and Renal Clearance Modeling
• Drug-Drug Interactions and Transporter Modeling
• Case Studies in Computational ADME: Successes and Challenges
• Software and Tools for Computational ADME Modeling
• Applications of Computational ADME in Drug Development and Regulatory Affairs

Assessment

The evaluation process is conducted through the submission of assignments, and there are no written examinations involved.

Fee and Payment Plans

30 to 40% Cheaper than most Universities and Colleges

Duration & course fee

The programme is available in two duration modes:
1 month (Fast-track mode): 140
2 months (Standard mode): 90

40% Cheaper

Our course fee is up to 40% cheaper than most universities and colleges.

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Awarding body

 The programme is awarded by London School of International Business. This program is not intended to replace or serve as an equivalent to obtaining a formal degree or diploma. It should be noted that this course is not accredited by a recognised awarding body or regulated by an authorised institution/ body.

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  • Start this course anytime from anywhere.
  • 1. Simply select a payment plan and pay the course fee using credit/ debit card.
  • 2. Course starts
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Got questions? Get in touch

Chat with us: Click the live chat button

+44 75 2064 7455

admissions@lsib.co.uk

+44 (0) 20 3608 0144



Career path

Career Role (UK) Description
Computational ADME Scientist Develops and applies computational models to predict ADME properties of drug candidates, influencing early drug discovery. High demand for advanced modelling skills.
Senior ADME/DMPK Modeller Leads projects, mentors junior staff, and ensures high-quality ADME/DMPK modelling support for drug development programs. Requires strong leadership and advanced computational ADME skills.
Pharmacometrician (ADME focus) Applies pharmacokinetic and pharmacodynamic modelling techniques to analyze ADME data and support clinical trial design. Strong statistical skills crucial.
In Silico ADME Consultant Provides expert computational ADME advice and support to pharmaceutical and biotechnology companies. Excellent communication and problem-solving skills needed.

Key facts about Global Certificate Course in Computational ADME Modeling

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This Global Certificate Course in Computational ADME Modeling equips participants with the essential skills and knowledge to predict and optimize the absorption, distribution, metabolism, and excretion (ADME) properties of drug candidates. The course focuses on utilizing computational techniques to accelerate drug discovery and development.


Learning outcomes include a deep understanding of various in silico ADME prediction methods, proficiency in using relevant software and databases, and the ability to interpret and apply computational ADME data for informed decision-making in drug design. Participants will gain practical experience through hands-on exercises and case studies.


The duration of the course is typically flexible, often structured to accommodate working professionals. A typical course might span several weeks or months, depending on the intensity and specific curriculum. The program's flexible online format facilitates accessibility for participants worldwide.


The pharmaceutical and biotechnology industries significantly benefit from expertise in Computational ADME Modeling. This certificate program enhances employability and career advancement prospects within these sectors, offering graduates a competitive edge in the field of drug discovery. The course covers topics crucial to regulatory submissions and successful drug development, including pharmacokinetics (PK) and pharmacodynamics (PD) modeling.


Graduates of this Global Certificate Course in Computational ADME Modeling will possess in-demand skills, enabling them to contribute effectively to the development of safer and more effective medicines. The program provides a strong foundation in cheminformatics and bioinformatics, critical aspects of modern drug development.

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Why this course?

A Global Certificate Course in Computational ADME Modeling is increasingly significant in today's pharmaceutical market. The UK's burgeoning biotech sector, coupled with stringent regulatory requirements, demands professionals proficient in in silico ADME prediction. This skillset is crucial for streamlining drug discovery and development, reducing costs, and accelerating time-to-market.

Recent reports suggest a growing need for computational scientists in the UK. While precise figures are hard to pin down, estimates indicate a substantial increase in demand, fueled by investment in AI-driven drug discovery. This scarcity of skilled professionals creates a competitive advantage for those possessing expertise in computational ADME modeling techniques such as QSAR, machine learning, and physiologically based pharmacokinetic (PBPK) modeling.

Year Estimated Demand
2022 1500
2023 1800
2024 2200

Who should enrol in Global Certificate Course in Computational ADME Modeling?

Ideal Audience for Global Certificate Course in Computational ADME Modeling
This computational ADME modeling course is perfect for scientists and researchers involved in drug discovery and development. With the UK pharmaceutical industry employing over 70,000 people (source needed), the demand for professionals skilled in ADME prediction and simulation is high. Are you a pharmacokineticist, medicinal chemist, or toxicologist seeking to enhance your skills in in silico modeling techniques? Then this certificate is designed for you. Gain a competitive edge by mastering absorption, distribution, metabolism, and excretion (ADME) modeling, streamlining drug development processes and improving prediction accuracy.