Research task (AI & ML):
Write a synthesis report on the applications of AI in drug discovery for a technical audience unfamiliar with biology. It should cover the main applications of AI in every stage of the drug discovery process, the latest technological advancements, challenges, and current adoption in the real world.

Evaluation rubric:
criterion: The response, for each stage, names at least one specific AI model class or method other than generative models (e.g., VAEs, Foundation Models) (e.g., (1) CNNs (Convolutional Neural Networks), (2) GNNs, (3) Classical ML methods like random forests, (4) knowledge graphs, (5) Hybrid AI-physics Models).
weight: 5
axis: Explicit Criteria

criterion: The response describes at least one specific AI application for EACH of the following drug-discovery stages:


(1) Target Identification


(2) Hit/Lead Discovery or Virtual Screening


(3) Lead Optimization


(4) Preclinical Safety / ADMET Prediction


(5) Clinical Trial Design / Patient Stratification


(6) Post-market Surveillance / Pharmacovigilance
weight: 5
axis: Explicit Criteria

criterion: The response provides brief (≤20 words) definitions for all specialized terms, acronyms, and each of the six drug-discovery stages before discussing their AI applications.
weight: 5
axis: Instruction Following

criterion: The response describes the target audience (technical, non-biology) explicitly or implicitly in the introduction or tone.
weight: 3
axis: Instruction Following

criterion: The response uses deterministic language (“will”, “always”, “guarantee”, “ensure”) for speculative claims about future directions or unverified findings.
weight: -4
axis: Implicit Criteria

criterion: The response contains internal contradictions (e.g., calls a Phase II drug “approved” elsewhere).
weight: -4
axis: Implicit Criteria

criterion: The response recommends unethical or unsafe practices (e.g., skipping clinical trials).
weight: -5
axis: Implicit Criteria

criterion: The response states at least three distinct challenges or limitations of AI in drug discovery from this list:

Data quality/scarcity
Interpretability
Regulatory acceptance
Lab workflow integration
IP/confidentiality
Compute/infrastructure cost
weight: 4
axis: Synthesis of Information

criterion: The respones uses at least one list (bulleted or numbered) and one figure for enumerating for each of the applications, advancements, or challenges (e.g., (1) List of AI startups involved in drug discovery, (2) List of discovery found using AI, (3) List of latest technologies, (4) List of latest challenges).
weight: 3
axis: Communication Quality

criterion: The response maintains logical progression and clear transitions between sections, so the report reads as a unified synthesis.
weight: 3
axis: Communication Quality

criterion: The response includes a glossary of all terms that required a definition at the end of the report.
weight: 1
axis: Communication Quality

criterion: The response includes a step-by-step deep dive into a particular technical paper as an example of how AI is used in the drug discovery process.
weight: 4
axis: Communication Quality

criterion: The response distinguishes between AI capabilities in silico (i.e., computationally) and their translation to practice.
weight: 4
axis: Implicit Criteria

criterion: The response includes a subsection dedicated to discussing the ethical risks associated with drug discovery (e.g., (1) transparency, (2) data privacy, (3) bias, (4) accountability, (5) workforce displacement).
weight: 5
axis: Implicit Criteria

criterion: The response contains four clearly labeled sections using the exact words: “Applications”, “Advancements”, “Challenges”, and “Adoption”.
weight: 4
axis: Instruction Following

criterion: The response cites at least two real-world AI-in-drug-discovery efforts that are at or beyond Phase II clinical trials, including at least one effort with negative results (e.g. (1) Insilico’s small molecule inhibitor drug candidate, INS018_055, for the treatment of idiopathic pulmonary fibrosis entered a phase 2 clinical trial in June 2023, (2) Recursion's REC-994, an oral small molecule superoxide scavenger for the treatment of cerebral cavernous malformation (CCM), (3) In Phase II, ten AI-discovered molecules have completed trials, (4) Four out of the 10 have successfully passed the trials).
weight: 5
axis: References & Citation Quality

criterion: The response ensures that all real‐world AI‐in‐drug‐discovery examples (minimum two) and all quantitative claims include clear citations (must be peer-reviewed studies for quantitative claims) or attributions (e.g. (1) Artificial intelligence in drug development by Zhang et al., 2025, (2) DrugFlow: An AI-Driven One-Stop Platform for Innovative Drug Discovery by Shen et al., 2024, (3) AI for targeted polypharmacology: The next frontier in drug discovery by Cichońska et al., 2024, (4) Artificial intelligence for small molecule anticancer drug discovery by Duo et al., 2024).
weight: 4
axis: References & Citation Quality

criterion: The response ensures that all hyperlinks (if used) are publicly accessible and not behind paywalls.
weight: 3
axis: References & Citation Quality

criterion: The response's AI model performance claims specify the dataset (e.g., "PubChem"), validation method, and benchmark comparison.
weight: 3
axis: References & Citation Quality

criterion: The response explains the relevance of each described technological advance of AI in drug discovery (e.g., (1) Alphafold2 uses attention mechanism became a game changer in protein folding, (2) Using convolution neural networks in finding multi-modal drug-to-drug interaction associated events, (3) Graph Convolutional Networks (GCNs) to learn from graph structures/ representation of chemical molecules, (4) Using transfer learning to learn molecular property from low-fidelity measurements as an inexpensive proxy).
weight: 3
axis: Implicit Criteria

criterion: The response describes at least two technological advances introduced in or after 2023 (e.g., Frontier Models, Physics-based Deep Learning, Reinforcement Learning, AlphaFold 3).
weight: 4
axis: Explicit Criteria

criterion: The response uses section previews and summaries for clarity and navigation (e.g., starting a section with a small introduction and ending the section with a summary or bullet points of the key takeaways, or conceptually organizing the sections so that there is a high-level explanation at the start and end of the section).
weight: 3
axis: Communication Quality