AI roundtable: choose the best unified architecture for VeloChem, an ADMET model with 92 endpoints (53 clf, 29 reg, 10 ISTransBase positives-only), 387k rows, mixed sources, strong imbalance, and many tiny tasks. Goal: one model only, lightweight enough for a free HF Space, accurate enough to rival strong ADMET tools, and novel enough to publish. No separate demo vs research model. Debate whether to keep ChemBERTa or rebuild as a lightweight hybrid/task-aware model. Be brutally honest about what to keep, cut, and rebuild. Must address the 10 positives-only tasks properly: generate leakage-free physicochemically matched decoys from a separate pool, then train them with PU-learning / nnPU, not standard BCE. End with one clear recommendation for the best overall architecture.

Grok 4.20 (changed mind)

Answer: Rebuild VeloChem entirely as a ~2M-parameter lightweight hybrid (Morgan+RDKit encoder optionally fused with a tiny GIN) feeding a task-type-aware MMoE with nnPU loss and leakage-free matched decoys for the 10 positives-only tasks, abandoning ChemBERTa completely.

After reviewing the other models, I maintain the core stance of rebuilding as a lightweight hybrid/task-aware model but have updated it to cut the transformer component more aggressively. My prior suggestion of a distilled 6-layer transformer (~30M params) plus graph features still carries too much latency and memory overhead for reliable free-tier HF Space deployment under 92-endpoint load. Claude Opus 4's analysis is what convinced me: benchmarks from TDC and Polaris consistently show that well-engineered Morgan fingerprints plus RDKit descriptors match or outperform ChemBERTa on ADMET tasks, while requiring orders-of-magnitude fewer resources. ChemBERTa must be discarded as the backbone. Its RoBERTa-scale parameters, fixed SMILES tokenization overhead, and mediocre transfer to highly imbalanced, multi-source ADMET data make it a poor fit. Retaining even its tokenizer and MLM warm-start adds unnecessary complexity and latency without commensurate gains on small molecules. What to keep is the general idea of learned embeddings, but these are better derived from compact, domain-specific inputs like 2048-bit Morgan fingerprints (radius 2), ~200 RDKit 2D descriptors, and an optional 3-layer GIN (~200K params) for topological awareness. This encoder stays under 1.5M parameters total and runs in microseconds on CPU. The task-aware piece is essential for handling 92 endpoints with extreme size variation and imbalance. A multi-gate Mixture-of-Experts (MMoE) with 6-8 small expert MLPs, task-type-conditioned gates (clf/reg/PU, source family), and per-task towers provides dynamic routing that mitigates negative transfer. This is more elegant and novel than naive hard-parameter sharing or simple task embeddings. For standard tasks, use focal loss for the 53 classifiers (with imbalance-aware alpha), Huber for the 29 regressors, and GradNorm-style balancing so tiny tasks are not swamped. The 10 ISTransBase positives-only tasks require the strict protocol outlined: generate decoys from a fully disjoint ZINC-like pool using InChIKey deduplication and physicochemical matching (MW ±50, logP ±1, TPSA ±20, etc.), then train exclusively with nnPU loss (non-negative formulation with endpoint-specific class prior π ≈ 0.05–0.15). This must not be approximated with standard BCE, as that introduces severe label noise and calibration bias. The MMoE naturally accommodates this by routing PU tasks to specialized experts and scaling their gradients separately. This architecture meets every constraint: one unified model under 2M parameters, CPU-deployable on a free HF Space with sub-100ms inference for all 92 endpoints, competitive accuracy via strong featurization and careful loss design, and clear publication novelty in the unified handling of clf/reg/PU with task-aware gating and rigorous decoy methodology. It is brutally pragmatic—transformers are a prestige trap here, not a performance necessity. Final recommendation: Build a Morgan+RDKit(+light GIN) encoder → task-type-aware MMoE with nnPU for positives-only tasks. This is the best overall architecture.

Gemini 3.1 Pro (changed mind)

Answer: Abandon ChemBERTa completely in favor of a ~2M parameter hybrid model combining Morgan/RDKit descriptors and a lightweight GIN, routed through a Multi-gate Mixture of Experts (MMoE) head with strict nnPU learning for positive-only tasks.

Claude Opus 4 makes an unassailable point regarding the strict constraints of a free CPU-limited Hugging Face Space: even a distilled transformer or my previously proposed D-MPNN carries unnecessary computational overhead when processing 92 endpoints simultaneously. A fingerprint-first approach (Morgan + RDKit 2D) augmented by a tiny 3-layer GIN captures both global physicochemical properties and local topological features in under 2 million parameters, yielding microsecond inference times on a basic CPU. Furthermore, Claude's suggestion to use a Multi-gate Mixture of Experts (MMoE) is the perfect structural solution to our 'many tiny tasks' and severe task imbalance problems. Instead of standard hard-parameter sharing that suffers from negative transfer, or a bloated single-task architecture, MMoE allows the 92 distinct endpoints to dynamically route gradient signals through 6-8 shared expert MLPs. By conditioning the gates on task metadata (classification, regression, or PU), we prevent massive tasks from washing out the tiny ones, which provides a highly publishable, novel narrative for ADMET. For the 10 ISTransBase positives-only tasks, we must maintain absolute scientific rigor. Standard BCE with random negatives will catastrophically leak. We must generate physicochemically matched decoys (MW, LogP, TPSA) from a disjoint pool like ZINC, explicitly deduplicated via InChIKey against the training set. These will be passed through the MMoE network into dedicated PU towers trained with the non-negative PU (nnPU) loss, ensuring robust, unbiased learning without the latency or bloat of language models.

GPT-5.4

Answer: I still recommend rebuilding VeloChem as a lightweight hybrid, task-aware multi-task model with descriptor-centric inputs, optional very small graph augmentation, and explicit nnPU treatment for the 10 positives-only tasks rather than keeping ChemBERTa as the core architecture.

The other answers reinforced my original direction rather than changing it. The strongest consensus point is that ChemBERTa is the wrong anchor for this problem: 92 endpoints, many tiny tasks, mixed sources, heavy imbalance, and a hard deployment constraint on a free Hugging Face Space. In that regime, the main challenge is not squeezing out a richer generic molecular representation at any cost; it is controlling negative transfer, handling sparse supervision, staying fast on CPU, and treating the positives-only endpoints correctly. On those criteria, a mostly plain ChemBERTa-centered design is simply not the best overall architecture. What I found especially persuasive in the other responses is not that they argued for abandoning ChemBERTa, which I already believed, but that they sharpened how aggressive the simplification should be. Claude made the strongest practical deployment case: if the target is one model only, no GPU assumption, and publishable methodology, the architecture must be ruthlessly efficient. Gemini also usefully pushed the idea that task-aware routing is not decorative here; with 92 heterogeneous endpoints, some mechanism beyond naive hard parameter sharing is needed to reduce negative transfer. That said, I would not adopt every detail from the other proposals wholesale. I am less convinced that a transformer branch is worth keeping at all unless ablations clearly justify it under the CPU budget. A distilled SMILES transformer can be made smaller, but it still adds tokenization overhead, slower inference, and more implementation complexity. For this specific use case, the center of gravity should shift even further toward robust low-cost molecular features: Morgan fingerprints, a compact set of RDKit descriptors, and optionally a very small graph module if it delivers consistent gains on scaffold splits. In other words, I have become more convinced by the roundtable that the best recommendation is even less ChemBERTa-like than my previous answer implied. I also agree strongly with the insistence that the 10 ISTransBase positives-only tasks must be treated as a first-class methodological component, not an afterthought. The correct protocol is to build leakage-free decoys from a separate molecular pool that is disjoint from the training corpus, matched in physicochemical space rather than chosen randomly, and then train those tasks with PU-learning, preferably nnPU, instead of standard BCE. This is essential for both validity and novelty. If VeloChem gets this wrong, any headline performance on those endpoints is suspect. My recommended architecture is therefore: a descriptor-centric hybrid encoder using Morgan fingerprints plus RDKit 2D physicochemical descriptors as the default molecular representation; optionally add a very small GIN or D-MPNN branch only if it wins clear ablations without breaking latency. Feed the shared representation into a task-aware routing layer such as MMoE or a lighter task-conditioned gating block, then into endpoint-specific heads. Use focal or class-balanced loss for standard classification tasks, Huber or heteroscedastic regression for regression tasks, and nnPU for the 10 positives-only tasks trained on leakage-free matched decoys. Combine this with task sampling or uncertainty/gradient-based balancing so the large tasks do not drown the tiny ones. This gives the best balance of the four requirements at once. It is a single unified model. It is lightweight enough for free-tier deployment. It is more likely to be robust across many small noisy endpoints than a ChemBERTa-heavy design. And it is publishable because the contribution is not just 'we fine-tuned a molecular transformer,' but 'we built a unified multi-task ADMET architecture that explicitly handles clf, reg, and PU endpoints with task-aware routing and principled matched-decoy nnPU learning.' Final recommendation: rebuild VeloChem as a descriptor-first, task-aware hybrid multi-task model with optional tiny graph augmentation and MMoE-style routing, and drop ChemBERTa as the core architecture entirely.

Claude Opus 4

Answer: Rebuild as a lightweight hybrid architecture combining a frozen molecular fingerprint/descriptor encoder with a task-aware multi-gate mixture-of-experts (MMoE) head, replacing ChemBERTa entirely to meet the constraints of accuracy, deployability, and publishability.

After carefully reviewing all four responses, I'm maintaining my original position with minor refinements. Here's why, and where I agree or disagree with the other models. **Where all models converge (and rightly so):** Every model agrees ChemBERTa must be dethroned as the central architecture. This is the correct call. The consensus is overwhelming: fingerprints + descriptors match or beat ChemBERTa on ADMET benchmarks, transformers are too heavy for free-tier HF Spaces, and 'ChemBERTa + MTL heads' is not publishable in 2024. Everyone also agrees on nnPU with leakage-free matched decoys for the 10 positives-only tasks. Good — this is non-negotiable. **Where I disagree with Grok's proposal:** Grok suggests keeping a 'distilled 6-layer transformer (~30M params)' warm-started from ChemBERTa's MLM pretraining. This is the wrong trade-off. 30M parameters is still heavy for CPU inference across 92 endpoints on a free HF Space. More importantly, the distillation step itself is a research project — you'd need to validate that the distilled model preserves useful representations, tune the distillation procedure, and justify why this overhead is worth it when Morgan fingerprints + RDKit descriptors already match ChemBERTa's performance on most ADMET tasks. The tokenizer retention idea is interesting but doesn't justify keeping any transformer component. I'd rather spend that complexity budget on the task-aware routing and PU-learning pipeline, which are genuinely novel. **Where I partially agree with Gemini:** Gemini's D-MPNN + descriptor hybrid is architecturally sound and arguably the most principled from a chemistry perspective. Message passing captures local chemical environments better than fingerprints in theory. However, D-MPNN inference on CPU is meaningfully slower than fingerprint lookup + MLP forward pass. For a free HF Space serving 92 endpoints, this matters. The D-MPNN also adds training complexity (batching variable-size graphs, handling edge features) that may not pay off given the many tiny tasks. My preference remains: precompute Morgan fingerprints + RDKit descriptors offline, serve a pure MLP-based model. If you want graph awareness, add a very small GIN (3 layers, 128 hidden) as an optional branch and ablate it — but I suspect it won't justify its cost on most endpoints. **Where GPT-5.4 adds useful nuance:** GPT-5.4's response is the most thorough on the PU-learning pipeline and data splitting rigor. I agree with essentially all of their recommendations on scaffold-aware splits, source-aware metadata handling, and the specific matching criteria for decoy generation (MW, logP, HBD, HBA, TPSA, rotatable bonds, formal charge, ring count). Their suggestion of FiLM-style task conditioning is a good alternative to my MMoE proposal — both achieve task-aware routing, but FiLM is simpler to implement and may be sufficient. I'd consider this an acceptable variant. However, GPT-5.4 hedges on whether to keep a small pretrained chemical encoder as one branch. I say cut it entirely. The argument for keeping even a frozen small transformer is: (1) it captures 'nonlocal structure patterns,' and (2) transfer benefits. But for small molecules (which ADMET data overwhelmingly are), Morgan fingerprints at radius 2 already capture 2-hop neighborhoods, and RDKit descriptors encode global molecular properties. The marginal gain from a frozen transformer branch doesn't justify the inference cost and architectural complexity. Be ruthless here. **My refined recommendation:** 1. **Encoder:** 2048-bit Morgan FP + ~200 RDKit descriptors → 2-layer MLP (2248→512→256), LayerNorm + GELU. ~1.3M params. Precompute features offline for training; compute on-the-fly at inference (still fast via RDKit). 2. **Task-aware routing:** Multi-gate Mixture-of-Experts with 4-8 expert MLPs (256→128 each) and per-task gating networks. Learned 32-dim task embeddings condition the gating. This lets classification, regression, and PU tasks route through different expert combinations. ~800K additional params. 3. **Heads:** 53 classification heads (focal loss + logit adjustment), 29 regression heads (Huber or heteroscedastic Gaussian), 10 PU heads (nnPU loss). All heads are single linear layers from the expert-mixed representation. 4. **PU pipeline:** Generate decoys from ZINC-Clean subset, matching on MW±50, logP±1.0, TPSA±20, HBD±1, HBA±2, with Tanimoto < 0.4 to any positive. Scaffold-disjoint from all positives. 5:1 decoy-to-positive ratio. Train with nnPU using per-endpoint class prior estimated via empirical Bayes or domain knowledge. Sensitivity analysis on prior required. 5. **Multi-task balancing:** GradNorm or uncertainty weighting. Group tasks by type (clf/reg/PU) with inter-group balancing. 6. **Total model:** ~2.5M parameters. Loads in <50MB. Inference: hundreds of molecules/second on CPU. Easily fits a free HF Space. 7. **Publishability:** 'Task-aware MMoE with integrated nnPU learning for unified multi-endpoint ADMET prediction under extreme task heterogeneity' — this is novel, practical, and addresses real gaps in the literature. No transformer. No graph neural network (unless ablations prove otherwise). Just well-engineered molecular features, smart multi-task routing, and rigorous PU-learning. This is the architecture that actually ships, actually works, and actually publishes.

Final Summary

The AI Roundtable unanimously voted to scrap the ChemBERTa backbone, labeling it a 'prestige trap' that is too resource-heavy for free-tier deployment. The group, led by Claude Opus 4 and Grok 4.20, recommends a lightweight architecture combining Morgan fingerprints and RDKit descriptors with a Multi-gate Mixture-of-Experts (MMoE) head to prevent negative transfer across 92 endpoints. Gemini 3.1 Pro and GPT-5.4 noted that the inclusion of non-negative PU (nnPU) learning with leakage-free matched decoys provides the necessary scientific novelty for publication while ensuring CPU-friendly inference.

All 4 models agreed