Vibe coding 101 for drug discovery scientists

The pharmaceutical industry stands at an inflection point. Laboratory scientists who once spent weeks waiting for IT departments to build custom analysis tools can now create sophisticated applications themselves – often in hours rather than months. This isn’t science fiction; it’s the reality of ‘vibe coding,’ a paradigm shift in software development that is particularly transformative for drug discovery workflows.

What is vibe coding?

Vibe coding represents a fundamental departure from traditional programming. Instead of memorising syntax, debugging cryptic error messages and wrestling with documentation, scientists describe what they want to build in natural language – and AI coding assistants handle the implementation details. It is like having a senior software engineer sitting beside you, translating your scientific requirements into production-ready code in real time.

The term ‘vibe coding’ captures the essence of this approach: you set the direction (the ‘vibe’) and the AI handles the technical execution. For drug discovery scientists, this means you can focus on the science – the experimental design, data relationships and analytical logic – while the AI manages the software architecture, database schemas and API integrations.

I have created an interactive web application that demonstrates this concept in action, walking through a real-world pharmaceutical use case: building a laboratory experimental document monitoring system. I encourage you to explore it and click through the different tabs as we discuss what makes this approach revolutionary.

Why drug discovery scientists should care

Traditional software development created an artificial barrier between scientific insight and computational implementation. A medicinal chemist might identify patterns in Structure–Activity Relationship (SAR) data that could accelerate lead optimisation – but translating that intuition into a functional analysis tool required months of development cycles, stakeholder meetings and technical debt accumulation.

Vibe coding collapses this timeline dramatically. The same chemist can now describe their analytical requirements to an AI assistant. For example: “I need to identify chemical scaffolds with unexpected potency increases when specific substituents are added at the R2 position, cross-referenced against our historical compound library.” Within hours that chemist will have a working prototype that queries their databases, performs the structural analysis and generates interactive visualisations.

The Overview tab of the web app introduces this concept through a concrete example from my own work: a system for monitoring wet lab experimental documents. This isn’t a toy problem; it’s the kind of complex, domain-specific application that pharmaceutical companies typically outsource or deprioritise because the development cost seems prohibitive.

The pharmaceutical use case: laboratory knowledge graphs

The web app centres on a sophisticated problem familiar to any drug discovery scientist: making sense of hundreds of experimental write-ups scattered across PDFs, electronic lab notebooks (ELNs) and institutional memory. Each experiment builds on previous work, uses specific equipment and protocols, and generates insights that inform future studies. Yet this rich network of relationships remains largely implicit, locked in unstructured text.

The Problem Statement tab articulates this challenge precisely. We have all experienced the frustration of rediscovering that a colleague ran an identical experiment six months ago, or failing to notice that three different teams are all struggling with the same recalcitrant protein expression protocol. The solution requires more than simple document management – it demands semantic understanding of experimental relationships.

What makes this problem perfect for demonstrating vibe coding is its complexity. The application needs to:

1. extract structured data from messy PDFs (containing chemical structures, instrument parameters and experimental narratives)
2. identify semantic relationships between experiments (shared equipment, temporal sequences, contradicting results)
3. store these relationships in a queryable graph database
4. run intelligent agents that detect biases, patterns and experimental gaps.

Traditional development would require a cross-functional team: backend engineers for the database, data scientists for the machine learning models, frontend developers for the interface and crucially domain experts to translate scientific requirements at every step. With vibe coding, a single scientist with domain expertise can architect the entire system.

The technical architecture: sophistication without suffering

The Technical Architecture tab reveals something remarkable: the system uses genuinely sophisticated technology. It is not merely a CRUD (create, read, update, delete) application with a wrapper. The architecture includes:

• FAISS vector databases for semantic similarity search across experimental write-ups
• GROQ API integration for large language model analysis of document relationships
• Neo4j graph database for storing and querying multi-hop experimental sequences
• LangGraph agent systems for autonomous pattern detection and bias analysis.

In traditional development, each of these technologies requires specialised expertise. FAISS demands understanding of high-dimensional vector spaces and approximate nearest neighbour algorithms. Neo4j requires mastering Cypher query language and graph data modelling. LangGraph needs knowledge of state machines and agent orchestration patterns.

Yet with vibe coding, you  need not be an expert in any of these technologies. You need to understand your scientific problem deeply and you need to be able to describe what you want to accomplish. The AI assistant handles the technical implementation – but, more importantly, it makes architectural decisions that incorporate best practices you might not even know exist.

The Implementation Details tab shows this in practice, with actual code snippets demonstrating how semantic relationship extraction works. For a medicinal chemist or molecular biologist, this code might look intimidating – but that’s precisely the point. You  need not write this code yourself. You need to be able to explain to an AI assistant: “I want to find experiments that used similar protocols, even if they’re described differently, and I want evidence quotes so I can validate the relationships.” The AI translates that requirement into production-ready Python.

The honest conversation about limitations

One aspect that makes the web app particularly valuable is its refusal to engage in AI hype. The Challenges & Risks tab doesn’t sugarcoat the limitations of this approach. Relationship extraction isn’t 100 percent accurate – it’s around 85-90 percent precision. The system can hallucinate connections between experiments that don’t actually exist. Context window limitations mean very large experimental write-ups need to be grouped and analysed in pieces.

This honesty is crucial for scientists considering vibe coding. We are trained to think critically about methodological limitations and the same scrutiny applies here. The difference between vibe coding and traditional development isn’t perfection versus imperfection – it’s rapid prototyping with known limitations versus slow development that might never happen at all.

The web app includes a sophisticated testing framework (detailed in the Test Cases tab) that validates relationship extraction accuracy. This is software engineering best practice, but vibe coding makes it accessible: you describe what correct behaviour looks like and the AI implements the validation logic. For pharmaceutical applications where regulatory compliance matters, this testing infrastructure isn’t optional – and vibe coding makes it achievable even for small internal tools.

Multi-hop queries: the power of graph thinking

The Cypher Queries tab demonstrates something particularly powerful for drug discovery applications: multi-hop graph traversals. Consider this scenario: you’re investigating why a protein purification protocol is failing. You find one experiment where it worked and you want to trace backwards through the experimental lineage – what equipment was used, what prior experiments informed the protocol choice, what outcomes from those earlier experiments led to this specific approach.

In a traditional relational database, this would require complex JOIN operations across multiple tables; probably involving several rounds of queries. In a graph database with properly extracted relationships, it’s a single elegant query that returns the entire experimental genealogy. The web app shows how a medicinal chemist can express this need (“show me all experiments in the causal chain leading to experiment X”) and get executable Cypher queries that actually work.

This isn’t just academic – it’s how modern drug discovery operates. Hit-to-lead optimisation is iterative, with each compound iteration building on SAR insights from previous rounds. Understanding these dependencies is essential for making rational design decisions.

From problem to production: the workflow

The Workflow tab walks through the practical mechanics of building with vibe coding. You start by clearly articulating your problem in a structured document – not in technical jargon, but in the language of your domain. For the experimental document monitor, this means describing how wet lab scientists actually work: they write up experiments in PDFs, they reference previous work implicitly and use equipment and protocols with varying nomenclature.

This initial problem specification becomes the foundation. You then describe the desired system behaviour: “When I add new experimental PDFs, the system should extract structured information, identify relationships with existing experiments using semantic analysis, and store everything in a queryable graph.” The AI assistant translates this into a technical architecture document, which you can review and refine.

The Getting Started section provides concrete next steps, but the real insight is in understanding the iterative nature of vibe coding. You’re not writing a complete specification upfront and hoping the implementation matches; you’re having a conversation with an AI assistant that can show you working code, which you can test against your domain knowledge and then refine. This tight feedback loop is what makes complex systems buildable by domain experts.

The agent system: intelligence without infrastructure

The Agent Detection tab showcases one of the most powerful aspects of modern AI-assisted development: autonomous agents that monitor your experimental corpus and surface insights. The system includes agents that detect:

• equipment biases: are you over-relying on specific instruments, potentially introducing systematic bias?
• pattern recognition: are there consistent trends across experiments that suggest mechanistic insights?
• duplicate detection: have similar experiments been run without awareness, wasting resources?
• gap analysis: are there missing control experiments or incomplete hypothesis chains?

Building agent systems traditionally requires expertise in state machines, workflow orchestration and complex reasoning patterns. With vibe coding and frameworks like LangGraph, you describe what you want the agents to detect and how they should reason about the data. The system from the web app shows how these agents produce structured reports with evidence, confidence scores and actionable recommendations – all specified through natural language requirements rather than procedural code.

For pharmaceutical R&D leaders, this represents a fundamental shift. It is no longer necessary to choose between expensive custom software that will get built in 18 months and manual processes that don’t scale. You can have domain experts prototype intelligent monitoring systems rapidly, validate them on real data and iterate based on feedback.

Validation and testing: rigour in rapid development

The Test Cases tab addresses a crucial concern: how do you ensure quality when development happens this quickly? The answer lies in comprehensive testing frameworks that are themselves built through vibe coding. The web app demonstrates a test suite with 90+ validation cases covering different relationship types, edge cases and accuracy thresholds.

Each test case is defined in structured YAML files that specify the following: given these two experimental documents, the system should extract these specific relationships with at least this confidence level. The testing framework calculates precision, recall and F1 scores – standard machine learning metrics that pharmaceutical scientists understand.

This testing infrastructure is not an afterthought; it’s part of the initial design. When you describe your system requirements to an AI assistant, you also describe what correct behaviour looks like. The AI generates both the implementation and the tests that validate it. For GxP environments where software validation is mandatory, this testing-first approach is essential – and vibe coding makes it accessible.

The economic argument: from cost centre to competitive advantage

Beyond the technical capabilities, vibe coding fundamentally changes the economics of scientific software. Traditional custom development for pharmaceutical applications starts at $200K for simple tools and easily reaches millions for complex systems. The cost–benefit analysis rarely favours building domain-specific tools unless they’re absolutely critical or serve hundreds of users.

Vibe coding inverts this equation. The ‘cost’ is your time learning to effectively communicate requirements to AI assistants – measured in days or weeks, not months or years. The capital investment is an API subscription, not a development team. This means tools that would otherwise never have been built – because they serve only your research group or address a workflow bottleneck too niche to prioritise – are now economically viable.

The web app itself is a proof point. The experimental document monitoring system it describes would be a six-figure software project if developed traditionally. As a vibe coding exercise, it’s a weekend project for a scientist who understands the domain. Scale this across an R&D organisation and the productivity implications are staggering.

Beyond the hype: real limitations and real value

The web app’s Challenges section is deliberately sobering because vibe coding is not magic:

• LLM-based relationship extraction has false positives
• Graph database queries can miss subtle connections
• Agent recommendations require expert validation.

However, these limitations aren’t reasons to avoid vibe coding – they’re reasons to approach it with appropriate scientific rigour.

The value proposition is not that AI writes perfect software so humans don’t have to think; it is that AI handles technical implementation so humans can focus on domain expertise and validation. A medicinal chemist using vibe coding still needs a deep understanding of SAR analysis – they just don’t need to learn Django, PostgreSQL and React to build an SAR analysis tool.

Conclusion: the accessible revolution

The web application at https://vibe-coding-intro.vercel.app/ demonstrates that sophisticated scientific software is now within reach of the scientists who need it most. Explore the Architecture tab to see the technical sophistication involved, the Cypher Queries tab to understand the analytical power and the Workflow tab to grasp the practical path forwards.

Then consider the following:

What computational bottleneck in your research could be eliminated if you could describe what you need and have a prototype tomorrow? 

What synthetic route optimisation workflow could save weeks per compound series?

What protein crystallisation condition tracker could surface success patterns?

What metabolomics pipeline could be tailored to your specific mass spec configuration?

Vibe coding doesn’t promise to solve every software challenge; it promises something more valuable: it puts the power to try in the hands of people who understand the problems. For drug discovery scientists, that’s not just a technical capability – it’s a competitive advantage. The future belongs to organisations where domain experts can rapidly prototype, validate and deploy the computational tools their research requires.

The question is not whether AI can help you code. The question is: what will you discover when the computational tools finally match the speed of your scientific insight?

Understanding what that reality looks like in practice is now critical for every drug discovery organisation. What is working today, where are the real limitations and what will shape the next phase of adoption? Drug Target Review is bringing together leaders from biotech, pharma and academia who are deploying AI across research pipelines to answer those questions directly. Join our live webinar below to hear what they are seeing on the ground and put your own questions to the experts in the live Q&A. 

 

Meet the author

Dr Raminderpal Singh

Dr Raminderpal Singh is a recognised visionary in the implementation of AI across technology and science-focused industries. He has over 30 years of global experience leading and advising teams, helping early- to mid-stage companies achieve breakthroughs through the effective use of computational modelling. Raminderpal is currently the Global Head of AI and GenAI Practice at 20/15 Visioneers. He also founded and leads the HitchhikersAI.org open-source community and is Co-founder of the techbio, Incubate Bio. 

Raminderpal has extensive experience building businesses in both Europe and the US. As a business executive at IBM Research in New York, Dr Singh led the go-to-market for IBM Watson Genomics Analytics. He was also Vice President and Head of the Microbiome Division at Eagle Genomics Ltd, in Cambridge. Raminderpal earned his PhD in semiconductor modelling in 1997 and has published several papers and two books and has twelve issued patents. In 2003, he was selected by EE Times as one of the top 13 most influential people in the semiconductor industry.