Supercharging Discovery: How LLM Prompts Are Transforming Structural Biology and Cheminformatics

  • Motif/fingerprint discovery from single protein sequences.
  • Prediction of protein–protein interactions from sequence pairs.
  • Mapping from sequence to secondary structure and risk features.
  • Analog design suggestions for improved drug properties.
  • Pathway inference using protein functional annotations.
  • Rapid metabolic/toxicity profiling and mitigation strategies.
  • Patent mining and innovation mapping for biomolecular targets.
  • Synthetic route planning for cost-effective drug assembly.
  • Enzyme substrate range predictions from structural input.Structured extraction of key data (e.g., IC50) from literature.
  1. https://arxiv.org/html/2408.11363v1
  2. https://github.com/Yijia-Xiao/Protein-LLM-Survey
  3. https://www.nature.com/articles/s41598-025-99290-4
  4. https://arxiv.org/html/2408.11363v2
  5. https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btaf396/8215464
  6. https://arxiv.org/html/2405.06690v1
  7. https://arxiv.org/html/2410.05610v2
  8. https://openreview.net/forum?id=2nTzomzjjb
  9. https://arxiv.org/html/2409.04481
  10. https://pmc.ncbi.nlm.nih.gov/articles/PMC12076503/

The intersection of artificial intelligence and molecular science is fueling a revolution in protein structure analysis and drug discovery. Large language models (LLMs), like GPT-4 and Claude, are increasingly being used beyond text analysis — their ability to follow complex, multi-stage instructions allows them to accelerate real-world biopharma applications. Let’s explore 10 creative prompt engineering workflows that illustrate the scope and power of LLMs in structural biology and cheminformatics.

End-to-End Example Prompts for LLMs in Molecular Discovery

Below are ten sophisticated prompt workflows, each designed to work with standard LLMs accessible through Perplexity, OpenAI, and others. Each example provides a motivating use-case, example input, and expected output.

  1. Motif Discovery from Protein Sequence

    • Prompt: “Given this amino acid sequence, list known biological motifs, infer likely function, and suggest a related signaling pathway.”

    • Example Input: MKWVTFISLLFLFSSAYSRGVFRRDTHKSEIAHRFKDLGE

    • Sample Output: “Contains an SH3-binding motif; likely involved in eukaryotic signaling via proline-rich region interactions.”

  2. Protein–Protein Interaction Prediction

    • Prompt: “Given two sequences, analyze complementarity and predict the likelihood and context of protein–protein interaction.”

    • Example Input: Two FASTA-formatted sequences.

    • Sample Output: “Complementary binding motifs predicted; probable interaction in cytoskeletal scaffolding.”

  3. Secondary Structure Risk Profiling

    • Prompt: “Map this protein sequence to its secondary structure, correlating specific residues to structural or stability risks.”

    • Example Input: Amino acid sequence.

    • Sample Output: “Residue 24 (Pro) likely introduces helix bending; Cys residues suggest potential for disulfide-mediated stability.”

  4. Analog Suggestion for Drug-like Properties

    • Prompt: “Given this scaffold molecule, suggest five analogs with enhanced drug-likeness and briefly justify each proposal.”

    • Example Input: SMILES or InChI for a core structure.

    • Sample Output: “Adding a methyl group increases solubility; halogen substitutions can improve binding affinity.”

  5. Pathway Mapping from Experimental Protein Data

    • Prompt: “Based on these experimental protein annotations, trace the most probable signaling pathway and cite supporting literature.”

    • Example Input: Functional and localization annotations.

    • Sample Output: “Likely involved in the MAPK/ERK pathway; see PMID 12345678 for pathway evidence.”

  6. Rapid Toxicity Profiling for Small Molecules

    • Prompt: “Given the following molecule, enumerate likely metabolic liabilities and suggest chemical modifications to reduce toxicity.”

    • Example Input: Molecular structure or SMILES.

    • Sample Output: “Predicted aldehyde metabolite may be toxic; propose amide substitution.”

  7. Patents and Prior Art Search

    • Prompt: “List ten recent patents related to this protein target, summarizing the main innovation of each.”

    • Example Input: Target protein or gene.

    • Sample Output: A table of patents with titles, numbers, and summaries.

  8. Affordability and Synthesis Route Analysis

    • Prompt: “Suggest cost-effective, feasible synthesis routes for this small molecule, considering common lab reagents.”

    • Example Input: Compound name or SMILES.

    • Sample Output: “Three-step synthesis via Grignard addition followed by amide coupling.”

  9. Enzyme Substrate Promiscuity Insight

    • Prompt: “Given enzyme X, predict three likely substrate classes and rationalize the prediction based on active site features.”

    • Example Input: Enzyme name and structure data.

    • Sample Output: “Displays promiscuity toward primary amines due to flexible binding cleft.”

  10. Data Extraction from Biomedical Literature

    • Prompt: “Read the following abstract, extract all reported inhibitory concentrations (IC50) and summarize compound structures.”

    • Example Input: Biomedical abstract text.

    • Sample Output: “Compound A: IC50=42nM; pyridine-based core.”