🧬 THE NEXORA GROUP • CAREER INTERNSHIP

Drug Discovery Using AI & Machine Learning Internship

Build practical skills at the intersection of Artificial Intelligence, Machine Learning, Computational Biology, Cheminformatics and modern drug discovery. Work through structured projects involving molecular data, predictive modelling, virtual screening and AI-assisted drug research.

🧠 AI & ML for Drug Discovery
🧬 Molecular & Biological Data
💻 Python & Data Science
🔬 Computational Drug Discovery
📊 Real Project-Based Learning
👨‍🏫 Mentor Assistance
📜 Internship Certificate
🌎 100% Online & Remote

Why Choose This Internship?

✓
AI-Powered Drug Discovery Understand how AI and ML are applied to modern pharmaceutical research.
✓
Hands-On Projects Build practical projects around molecular data, prediction and screening.
✓
Industry-Relevant Tools Explore Python, RDKit, Jupyter, molecular datasets and AI workflows.
✓
Career Documentation Eligible interns can receive applicable completion documentation.
56 Structured Internship Modules
AI + ML Focused Learning
6 Duration Options
100% Online & Remote
ABOUT THE INTERNSHIP

Enter the Future of AI-Driven Drug Discovery

The Drug Discovery Using AI & Machine Learning Internship by TheNexoraGroup is designed for students, freshers and life-science learners who want to understand how artificial intelligence and computational methods are being applied to modern drug discovery.

The internship combines AI/ML fundamentals with molecular data, drug-target analysis, QSAR, molecular property prediction, virtual screening, cheminformatics and project-based learning.

You will progressively move from fundamentals to practical workflows and portfolio-oriented projects.

What You Will Experience

🧠
AI & Machine Learning
Build an understanding of supervised, unsupervised and deep learning approaches.
🧬
Molecular Data
Work with molecular structures, descriptors, fingerprints and biological datasets.
🔬
Drug Discovery Workflows
Explore target identification, screening, QSAR and drug-property prediction.
🚀
Portfolio Development
Build projects that demonstrate your practical understanding.
INTERNSHIP APPLICATIONS

Start Your AI Drug Discovery Journey

Applications are processed according to the currently available internship batch. Choose your internship duration and complete the registration form below.

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Registration Form

Fill in your details below to apply for the Drug Discovery Using AI & Machine Learning Internship.

This will appear on your certificate.
*Should be an active email id.
*This option allows you to postpone the program to a future date. Leaving it blank will apply the default schedule.
YOUR INTERNSHIP JOURNEY

From Fundamentals to AI Drug Discovery Projects

A structured pathway designed to take you from foundational concepts to practical computational drug discovery workflows.

01

AI & Biology Foundations

Understand drug discovery, biological targets, molecular data, AI concepts and the computational research ecosystem.

02

Python & Data Science

Work with Python, NumPy, Pandas, data cleaning, visualization and machine learning workflows.

03

Molecular AI

Explore molecular representations, descriptors, fingerprints, QSAR, property prediction and drug-target modelling.

04

Projects & Portfolio

Complete practical projects and document your work for a career-oriented portfolio.

TOOLS & TECHNOLOGIES

Build Skills With Modern AI Tools

The internship introduces a practical ecosystem used for AI, molecular data analysis and computational drug discovery.

🐍 Programming & Data

Python NumPy Pandas Matplotlib Jupyter Google Colab

🤖 AI & Machine Learning

Scikit-learn Regression Classification Clustering Deep Learning Model Evaluation

🧬 Cheminformatics

RDKit SMILES Descriptors Fingerprints Molecular Similarity QSAR

🔬 Drug Discovery

Virtual Screening Target Identification Drug-Target Interaction ADMET Docking Concepts Lead Optimization
COMPLETE INTERNSHIP CURRICULUM

56 Modules of AI Drug Discovery

Explore a structured curriculum covering AI, machine learning, molecular data, cheminformatics, computational drug discovery, virtual screening and project development.

01

Introduction to Drug Discovery

Drug discovery pipeline, stages and modern computational approaches.

02

Traditional vs AI-Driven Drug Discovery

Understand how AI is transforming research workflows.

03

Role of AI in Pharmaceutical Research

Applications of AI across modern drug research.

04

Drug Targets & Biological Systems

Understand targets, pathways and therapeutic relevance.

05

Introduction to Molecular Data

Explore chemical and biological datasets used in AI workflows.

06

Python for Drug Discovery

Python fundamentals for computational research.

07

Python Data Structures

Lists, dictionaries, functions and research-oriented data handling.

08

NumPy for Scientific Computing

Arrays, numerical operations and scientific calculations.

09

Pandas for Molecular Datasets

Data loading, cleaning and manipulation.

10

Data Visualization

Visualize scientific and molecular datasets.

11

Exploratory Data Analysis

Identify patterns, distributions and data-quality issues.

12

Data Cleaning & Preprocessing

Prepare datasets for reliable AI model development.

13

Machine Learning Fundamentals

Core concepts behind predictive machine learning.

14

Supervised Learning

Understand labelled-data prediction workflows.

15

Unsupervised Learning

Discover patterns and clusters in molecular data.

16

Regression for Property Prediction

Predict continuous molecular and drug-related properties.

17

Classification Models

Classify molecules using supervised learning approaches.

18

Clustering Molecular Data

Group compounds based on molecular characteristics.

19

Feature Engineering

Transform raw molecular data into useful model features.

20

Model Training & Testing

Train predictive models and evaluate their performance.

21

Model Evaluation Metrics

Accuracy, precision, recall, RMSE and related measures.

22

Overfitting & Underfitting

Understand model generalization and common modelling problems.

23

Cross Validation

Improve reliability of model evaluation.

24

Molecular Representation

Understand ways of representing chemical structures computationally.

25

SMILES Notation

Read and work with molecular structures in SMILES format.

26

Molecular Descriptors

Calculate computational descriptors from molecular structures.

27

Molecular Fingerprints

Generate fingerprints for molecular similarity and modelling.

28

RDKit Introduction

Explore cheminformatics workflows using RDKit.

29

Molecular Similarity

Compare compounds using computational similarity methods.

30

QSAR Fundamentals

Understand quantitative structure-activity relationship modelling.

31

QSAR Model Development

Build a practical predictive QSAR workflow.

32

Molecular Property Prediction

Predict drug-related molecular properties using ML.

33

Drug-Likeness Concepts

Understand molecular characteristics associated with drug candidates.

34

ADMET Fundamentals

Introduction to absorption, distribution, metabolism, excretion and toxicity.

35

ADMET Prediction with AI

Explore predictive modelling for drug-related ADMET properties.

36

Protein Structure Basics

Understand proteins, binding pockets and therapeutic targets.

37

Protein Data Bank

Explore publicly available protein structure information.

38

Protein Structure Preparation

Understand computational preparation before downstream analysis.

39

Drug-Target Interaction

Introduction to modelling interactions between molecules and targets.

40

Virtual Screening

Understand computational screening of compound libraries.

41

Molecular Docking Concepts

Understand docking, binding poses and scoring concepts.

42

Docking Workflow

Explore the computational workflow from preparation to analysis.

43

Deep Learning for Drug Discovery

Introduction to neural networks for molecular prediction.

44

Neural Networks & Molecular Data

Understand learning representations from molecular information.

45

AI-Based Lead Optimization

Explore how AI can assist compound prioritization and optimization.

46

Generative AI for Molecule Design

Introduction to AI-assisted generation of molecular candidates.

47

Drug Repurposing with AI

Explore computational approaches to identifying new therapeutic uses.

48

Network Pharmacology Concepts

Understand multi-target relationships and biological networks.

49

Project 1 — Molecular Property Predictor

Build an ML workflow for predicting molecular properties.

50

Project 2 — QSAR Activity Prediction

Create a predictive model for compound activity.

51

Project 3 — Molecular Similarity Engine

Build a practical similarity analysis workflow.

52

Project 4 — Virtual Screening Workflow

Develop a computational screening workflow for candidate prioritization.

53

Project 5 — Drug-Target Prediction

Develop an AI/ML workflow for interaction prediction.

54

Project Documentation

Document methodology, datasets, models, findings and limitations.

55

Portfolio Development

Convert project work into career-ready portfolio evidence.

56

Final Capstone Internship Project

Complete and present a structured AI drug discovery project.

PRACTICAL PROJECT EXPERIENCE

Build Projects That Show What You Can Do

Move beyond theory by applying AI, ML and computational approaches to drug discovery-oriented datasets and workflows.

🧬 Major Project 01

AI-Based Virtual Screening

Develop a structured workflow for analysing molecular candidates and prioritizing compounds using computational methods.

  • Molecular data preparation
  • Feature generation
  • Similarity analysis
  • Candidate ranking

🤖 Major Project 02

Drug-Target Interaction Prediction

Develop an AI/ML-based workflow to study potential relationships between drug molecules and biological targets.

  • Dataset preparation
  • Feature engineering
  • Model training
  • Prediction evaluation

📊 Minor Project

Molecular Property Prediction

Build a machine learning model capable of predicting selected molecular or drug-related properties.

  • EDA
  • Descriptor generation
  • Regression/classification
  • Model evaluation

🔬 Practice Project

QSAR Activity Prediction

Use molecular descriptors and machine learning to explore structure-activity relationships.

🧪 Practice Project

Molecular Similarity Analysis

Use molecular fingerprints and similarity approaches to compare candidate compounds.

🚀 Capstone

AI Drug Discovery Portfolio

Combine selected workflows into a documented final project that demonstrates your learning journey.

INTERNSHIP COMPLETION

Get Industry-Recognized Certification

Successfully complete the applicable internship requirements, assigned projects and activities to receive eligible internship completion documentation.

Drug Discovery using AI Internship Certificate
📜
Internship Certificate Certificate of successful internship completion
🏆
Project Completion Documentation Recognition of completed practical project work
💼
Portfolio Evidence Build documented evidence of your internship experience
🚀
Career Ready Experience Showcase your AI and drug-discovery project experience

Build Your Experience in AI-Powered Drug Discovery

Work through practical internship activities, projects and AI-focused applications related to modern drug discovery.

Enroll for Internship →
WHY THIS INTERNSHIP?

Don't Just Learn AI. Apply It.

Build interdisciplinary skills at the intersection of artificial intelligence, machine learning and life-science research.

01. AI Skills for Life Sciences

Understand how machine learning can be applied to molecular, biological and pharmaceutical datasets.

02. Portfolio-Oriented Learning

Complete practical work that can be documented and presented as evidence of your technical skills.

03. Interdisciplinary Exposure

Connect Python, machine learning, molecular science and computational drug discovery.

04. Practical Workflow

Follow a structured journey from datasets and modelling through projects and documentation.

05. Mentor Assistance

Get support during internship activities, assignments and project implementation.

06. Career Direction

Explore pathways related to computational biology, AI, bioinformatics, cheminformatics and pharmaceutical research.

Who Can Apply?

The internship can be suitable for:

🎓
Students & Freshers
Students looking to build practical AI and life-science skills.
🧬
Biotechnology & Life Sciences
Learners interested in computational drug discovery.
💊
Pharmacy & Healthcare Learners
Students wanting exposure to AI applications in drug research.
💻
Computer Science / AI Learners
Develop interdisciplinary applications of AI and ML.
NO HEAVY EXPERIENCE REQUIRED

Start With the Fundamentals

You do not need to be an expert in computational drug discovery to begin. The curriculum progressively introduces the concepts, tools and project workflows.

Basic familiarity with computers and an interest in AI, biology, pharmaceutical research or data science can be helpful.

Check Internship Registration →
INTERNSHIP COMPLETION

Complete Your Internship With Career Documentation

Eligible interns who successfully complete the applicable internship requirements, assigned work and project activities may receive applicable completion documentation from TheNexoraGroup.

📜 Internship Certificate 🏆 Project Completion Documentation ✉️ LOR Eligibility 💻 Project Portfolio 📚 Internship Learning Record
CAREER DIRECTION

Where Can These Skills Take You?

The skills covered can support further learning and project work across several interdisciplinary areas.

🤖 AI / ML for Life Sciences

Explore AI and machine learning applications in healthcare, biotechnology and pharmaceutical research.

🧬 Computational Biology

Build a foundation for computational analysis of biological and molecular datasets.

💊 Computational Drug Discovery

Develop exposure to AI-assisted drug screening, prediction and molecular analysis.

🔬 Bioinformatics

Extend your Python and data-analysis skills toward bioinformatics workflows.

🧪 Cheminformatics

Work with computational representations of chemical structures and molecular datasets.

📊 Research & Analytics

Strengthen your ability to analyse datasets and communicate technical project findings.

STUDENT EXPERIENCE

What Students Can Build

Use verified student feedback here before publishing specific names, companies or placement claims.

★★★★★

“The internship helped me connect machine learning concepts with pharmaceutical and molecular data. The project structure made the learning much more practical.”

Student Feedback India • AI / Life Sciences Learner
★★★★★

“I wanted to understand how AI can be used beyond traditional software projects. The drug discovery modules gave me a useful introduction to molecular AI workflows.”

Student Feedback India • Biotechnology Learner
★★★★★

“The combination of Python, machine learning and drug discovery made this internship especially interesting for my career goals.”

Student Feedback USA • Life Sciences / AI Learner
★★★★★

“The project-based structure helped me understand how datasets, models and computational research workflows connect together.”

Student Feedback USA • Computational Biology Learner
★★★★★

“I gained a better understanding of molecular descriptors, QSAR and virtual screening concepts and how machine learning can support these workflows.”

Student Feedback India • Pharmacy / Research Learner
★★★★★

“The curriculum gives a strong starting point for anyone interested in AI-driven healthcare and computational drug discovery.”

Student Feedback USA • Biomedical Data Learner
FREQUENTLY ASKED QUESTIONS

Everything You Need to Know

Who can apply for this internship?

Students, freshers and learners from AI, computer science, biotechnology, pharmacy, life sciences, bioinformatics and related backgrounds can explore the internship.

Is this internship online?

The program is designed for online/remote participation. Specific batch arrangements can be confirmed during registration.

Do I need previous AI experience?

No advanced experience is required to start. The curriculum progressively introduces the fundamentals before moving into projects.

What will I learn?

You will explore Python, data science, machine learning, molecular representations, RDKit, QSAR, molecular property prediction, virtual screening, drug-target interaction modelling and AI-assisted drug discovery.

Will there be projects?

Yes. The curriculum includes molecular property prediction, QSAR, molecular similarity, virtual screening, drug-target prediction and a final capstone-oriented project.

Can I mention the internship on my resume?

Eligible interns can document their internship experience and project work according to the applicable completion requirements.

How long is the internship?

Available internship duration options can include 1, 2, 3, 4, 5 or 6 months. Select your preferred duration during registration or confirm it with the internship team.

Will I receive a certificate?

Eligible interns who successfully complete the applicable requirements may receive internship completion documentation.

FINAL STEP

Ready to Start Your AI Drug Discovery Journey?

Build practical knowledge across Artificial Intelligence, Machine Learning and computational drug discovery. Complete the registration form below to apply.

⏰ INTERNSHIP APPLICATIONS

Applications Are Open — Apply Before Today's Deadline

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Registration Form

Complete your details to apply for the Drug Discovery Using AI & Machine Learning Internship.

This will appear on your certificate.
*Should be an active email id.
*This option allows you to postpone the program to a future date. Leaving it blank will apply the default schedule.
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Meet our Ex-Interns

Ayush Parasha- Student at Arya College of Engineering and IT

Pranav Joshi- Full Stack Developer

RITU SINGH-FullStack Developer

Jahruddeen Ansari-DevOps Engineer | AWS Practitioner Certification – Associate

Syed Kashif- New Delhi

Jonal Suthar – TCS | Systems Engineer

PRATIBHA KUMARI-RAMS Engineer @ Alstom

Ramavath Harish-NIT MEGHALAYA

Rahul Bansal – Product Manager at HP India

krishnavamshi Thadooru-Mern Full Stack Developer

Shubhanshu roy-MVJ College of Engineering, Bangalore, India

Deepthi Muthineni-Project Engineer at Wipro

Ayushi mallawat – Full Stack Web developer Intern at Solar Secure Solutions (Part of CWS)

Mayank Parmar-Intern at Solar Secure Solutions | Web developer| SVNIT (Part of CWS)

Ritesh Shukla- Full stack developer Intern at Soalr secure solutions(Part of CWS)

Mohit Kapil- IIT Roorkee’23 | Frontend Developer 

Mritunjay Mishra-Software Developer | Java | Python

Rachin-Associate QA Engineer at Empyra software solutions

Shivesh Singh – Software Engineering Student | AI/ML Enthusiast

Junaid Aftab – Full Stack Developer | Recruiter

Ayush Kumar – Computer Science Student | Learner | Generative AI

Sheebamol TG-Web developer/WordPress Developer

Uday Dalvi-Java/ web dev/ SQL developer

Saipranav Sapare-Front-End intern at Solar Secure Solutions (A part of CWS)

Some Top recruiters...

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FAQs

To begin, complete the registration form above by entering your name, email ID, and mobile number. Mention if you’re a student or a professional, along with your college or workplace details. You can also pick a convenient start date, and we’ll try our best to match it.

📩 Offer Letter: After we verify your registration, your offer letter will be issued and sent to your email within a few hours or the next morning.
📩 Welcome Package: On your chosen start date, you’ll receive login credentials and step-by-step guidance via email.
In case you have any queries, you can reach out through hr@corporatewebsolutions.in or WhatsApp/call +91-7618025090.

Definitely! Our support team assists with college-required documents such as attendance sheets, evaluation reports, progress updates, and project summaries throughout your program.

If you need to take a leave for any reason, simply email us or drop a WhatsApp message. You don’t have to wait for a response — just inform us and focus on your priority. Once you’re ready, you can easily resume your tasks.

Yes, a laptop or desktop with a good internet connection is mandatory for participating in this program.

Our programs feature pre-recorded video sessions and LIVE sessions both (Hybrid )  to offer maximum flexibility. Everyone — students, professionals, and mentors — can learn at their own pace without schedule conflicts.

Don’t worry there is an AI-powered assistant to help you with all your coding-related queries. Andie can check and debug code, generate new code, and even build entire projects for you.

This feature is exclusively available to registered candidates through their Task Portal.

Since mentors cannot be available 24/7, Andie was developed to bridge the gap. Located at the bottom-left corner of every module, this Full Stack Developer Bot is capable of writing, reviewing, and troubleshooting code anytime, ensuring you get instant solutions to your coding challenges, day or night.

This internship cum training opportunity is designed in such a way as from fresher to a professional anyone can get benefit out of it, The opportunity is divided into 15+ modules, and every Module contains 15-20 small tasks plus 1 project (in the last) based upon what you have learnt in that particular module. The complexity and practicality of your modules and projects will gradually increase, Timing is flexible, schedule yours accordingly.  After completing every project given in the modules, Submit within the deadlines.

A major part of this program contains a training part, however we have many projects (2 Major, 15 Minor and many practice projects) and many internal and external resources to sharpen the skills of the candidates. It is a self paced program, Training and internships will go simultaneously. After every Module, there will be a project related to it (may it be a minor, major or practice project)

We have 20+ projects to provide hands on Experience and sharpen the skills of the Interns. 

About Paid Projects_

Paid projects will be provided to the interns on two basis

1) The assignments/projects, which all students sends goes directly to our USA team. they evaluate all of them and create a kind of ranking sheet based upon some factors like quality, accuracy, deadline met etc. They keep this list with them for future paid projects.

2) The availability and number of the projects available.

The training is split into 15+ modules, each with 15–20 tasks and one project. As you progress, the complexity grows to ensure steady skill development. Projects must be submitted on time, and you can plan your schedule flexibly.

You’ll work on over 20 projects to build real-world experience.
👉 Paid Project Opportunity: Based on your performance (quality, speed, and accuracy), you may be eligible for paid tasks offered by our USA team, subject to availability.

Anyone interested can join! Basic knowledge of HTML, CSS, or JavaScript is a bonus but not compulsory. Graduates from any stream are welcome. There are no restrictions based on nationality, age, or background.

You decide your working hours! Whether you’re a student or a working professional, you can complete modules at your convenience. Plus, the internship is 100% remote — work from wherever you are most comfortable.

Once enrolled, you’ll have lifetime access to all training content, unless there’s a policy change in the future.

Absolutely! You’ll receive full training material, video tutorials, and 24/7 coding assistance before and during your project work.

Yes, but it depends! Stipends are offered based on your performance, the nature of the project, and USA team evaluations. Stipends typically range from ₹8,000 to ₹18,000 INR ($99 – $219) or higher, depending on project availability.

We actively hire top-performing interns for internal and client roles based on project availability. Additionally, we regularly post openings from our partners so you can apply even after completing the program.

Your certificate will be automatically prepared and scheduled to be emailed around your program completion date. Allow up to 48 extra hours if it falls on a weekend or holiday.

LORs are awarded to exceptional candidates based on strict performance criteria. If you require a LOR for special circumstances like studying abroad, you may also directly request it from HR.

Of course! Although not mandatory, if you find the program valuable, feel free to refer it to friends, classmates, or colleagues who might benefit.

Duration: Choose Your Internship Length —
1, 2, 3, 4, 5, or 6 Months (Online & Remote)
Stipend Given – Check Internship Overview
⏳ Time Left to Apply Today:
REGISTRATION FORM
Fill up the Below form to get registered in the program
This will appear on your certificate.
*Should be an active email id.
*This option allows you to postpone the program to a future date. Leaving it blank will apply the default schedule.

🔬 Drug Discovery using AI & Machine Learning Internship Learning, you will gain:

  • Machine Learning model development

  • Python for AI applications

  • Data preprocessing and analysis

  • Real-world AI project experience

  • Industry-ready technical skills

Explore more internship opportunities at The Nexora Grop