Computational Biology & Cheminformatics Expert

What you would do

• Analyze small-molecule and drug discovery datasets using cheminformatics and computational biology methods
• Curate, annotate, and validate chemical and bioactivity data from databases like ChEMBL and DrugBank
• Assess molecular interactions, pharmacological properties, and optimization strategies using computational methods
• Design and implement reproducible Docker environments and code-based benchmark tasks reflecting real drug discovery scenarios
• Assess AI-generated molecular predictions and recommendations for scientific accuracy and practical applicability

Who they want

• Deep expertise in drug discovery, molecular modeling, or chemistry-focused computational fields
• Strong Python coding proficiency for building tools and pipelines beyond analysis scripts
• Hands-on experience with cheminformatics platforms like RDKit, KNIME, Schrödinger, or OpenEye
• Track record in drug discovery work including SAR/QSAR analysis, ADMET prediction, or virtual screening
• Familiarity with Docker, Git, and GitHub for reproducible computational environments

Main skills

What the interview asks about

1. 1.ADMET prediction and interpretation

   ADMET properties critically determine whether compounds become viable drugs. Your judgment about which properties drive attrition and how to optimize them shows whether you understand real discovery constraints.

   For example: “A compound shows excellent potency against your target but predictions indicate poor oral bioavailability due to high lipophilicity and low solubility. Identify which modifications might improve ADMET without sacrificing binding affinity.”

2. 2.Dataset curation for AI training

   Biased or mislabeled training data produces unreliable AI models. Your ability to spot data quality issues and validate annotations determines whether models learn true relationships or artifacts.

   For example: “You receive 50,000 compound-bioactivity records from four different legacy studies with inconsistent assay protocols and quality control standards. Describe your curation strategy to create a cleaned dataset suitable for training.”

3. 3.Virtual screening and molecular docking

   Computational screening ranks millions of compounds by binding probability. Your understanding of scoring functions, binding mode reliability, and false positive rates determines whether predictions guide productive synthesis.

   For example: “You dock 2 million compounds and rank by binding score. The top 100 candidates show excellent predicted binding but historically only 5 percent exhibit experimental activity. What explains this discrepancy?”

4. 4.SAR and medicinal chemistry reasoning

   Structure-activity relationship analysis guides lead optimization by connecting chemical changes to biological outcomes. Your grasp of mechanism-property linkages shows whether your recommendations are scientifically grounded.

   For example: “Early screening shows replacing a methyl group with methoxy doubled potency, but replacing methyl with ethyl halved it. Propose a mechanistic explanation and discuss what structural variations you would test next.”

5. 5.Docker and reproducible task design

   Benchmark tasks must run identically across environments and produce objectively verifiable answers. Your ability to containerize complex workflows and write testable code determines whether AI systems solve realistic problems reliably.

   For example: “Design a Docker-containerized benchmark task where an AI system receives a query protein sequence and must predict which of 10 candidate compounds would have the best binding affinity. Specify what code, databases, and validation metrics you would include.”

6. 6.AI-generated chemistry evaluation

   Language models generate plausible-sounding but sometimes incorrect chemistry. Your ability to spot mechanistic errors, property prediction mistakes, or infeasible synthetic routes shows deep domain judgment.

   For example: “An AI system proposes a three-step synthesis to a target molecule and predicts excellent solubility, low clearance, and no CYP interactions. What specific evaluations would you perform to validate or refute these claims?”

A task you may get

Receive 100 actives and 100 inactives from a kinase campaign. Use cheminformatics to identify distinguishing structural features, propose a SAR hypothesis, recommend modifications, and design a Docker validation task for ranking compound activity.

How to prepare

• Review a recent drug discovery case study where computational predictions guided synthesis and discuss which analytical methods were most predictive
• Practice using RDKit or similar toolkit to manipulate molecular structures, generate chemical descriptors, and perform basic SAR analysis
• Compile examples of ADMET prediction tools and their accuracy limits, and prepare specific scenarios where predictions did or did not correlate with experimental data
• Study how virtual screening produces false positives and what additional validation methods help distinguish productive hits

The facts

Pay

$80–110/hr

Open to

Bangladesh, Hong Kong, India, Indonesia, Japan, Kazakhstan, Kyrgyzstan, Malaysia, Pakistan, Philippines, Singapore, Sri Lanka, Taiwan, Thailand, Uzbekistan, Vietnam, Austria, Belarus, Belgium, Denmark, France, Germany, Greece, Italy, Netherlands, Portugal, Russia, Spain, Switzerland, United Kingdom, Argentina, Brazil, Chile, Colombia, Mexico, Peru, Algeria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Tunisia, United Arab Emirates, United States, Canada, Nigeria, Kenya, South Africa, Ghana, Ethiopia

Field

Applied Engineering

Role type

Generalist

Posted

8/3/2026

Places left

50

We wrote this page from the public micro1 listing. It may be out of date, so read the full posting before you apply.