Open Problems

Molecular Modeling & Drug Design

Lossless Generalization Across Molecular Scaffold Diversity: Closing the Accuracy Gap on Unseen Compounds

Effect to explainPartly addressed
Possible candidate · 1/2 runs6 papers report this100% from 2025+

Generated automatically from the limitations stated in 6 papers (ICML, ACL, NeurIPS), listed under Evidence. It is not a paper, and it does not come from papers submitted to CSPaper.

The problem

Molecular generative and predictive models currently trade off reconstruction fidelity, chemical validity, and generalization: models that reconstruct accurately lose validity on novel chemistry, and models that stay valid degrade sharply on scaffolds, functional groups, and complex molecules absent or rare in training. This means any model's utility on genuinely new chemistry — the cases that matter most in drug discovery — is unpredictable and unmeasured per scaffold. Structurally, the field cannot yet certify that a model generalizes to uncorrelated compounds rather than interpolating within its training distribution.

Why it matters

Reliable application of generative molecular models to genuinely novel chemical space — novel scaffolds, large complex molecules — instead of only to analogs of training data, with per-scaffold confidence estimates.

Ways to approach it

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  1. 1

    Build a scaffold-stratified evaluation benchmark that holds out entire Bemis–Murcko scaffold classes and functional-group rarities, and measure reconstruction MAE, validity rate, and accuracy as a function of scaffold novelty, atom count, and rotatable-bond count. Deliverable: generalization-decay curves per scaffold class, published as a standardized suite.

  2. 2

    Introduce a validity-guaranteeing output layer or constrained decoder that enforces valence and aromaticity rules independently of reconstruction capacity, then measure whether validity reaches 1.000 without sacrificing reconstruction accuracy on large molecules (currently ~0.901 validity with degraded MAE).

  3. 3

    Augment training with scaffold-jumping curricula (systematically recombining fragments across held-out scaffolds) and measure whether generalization loss on unseen scaffolds shrinks relative to the stratified benchmark in (1).

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Why it might fail

Validity enforcement and reconstruction fidelity may be irreconcilable — the constrained decoder fixes validity but the capacity bottleneck on large molecules proves architectural, not regularization-based, so accuracy on novel scaffolds barely improves.

Sub-problems

  • Resolving the Reconstruction Capacity and Chemical Validity Trade-off in Molecular Autoencoders

    Current molecular graph autoencoders face an inherent trade-off between reconstruction accuracy on complex molecules and chemical validity of decoded structures. Enforcing hard validity constraints via autoregressive or grammar-based decoders limits expressive capacity and scales poorly to large, complex graphs, while unconstrained latent decoders suffer from invalid valencies and degraded property prediction. As a consequence, generative pipelines either discard a substantial fraction of generated candidates due to invalid chemistry or fail to accurately encode and reconstruct drug-like molecules with intricate ring systems and stereocenters.

Evidence

Each paper's own statement of the limitation, verbatim.

Nearest existing work

Related open problems

Molecular Modeling & Drug Design

Scope to testOpen

Benchmarks and Training Protocols for Size-and-Structure Transfer in Molecular ML

Molecular machine-learning models are trained and evaluated within narrow slices of chemical space — small, drug-like, scaffold-homogeneous molecules averaging ~44 atoms — so any deployment beyond that slice degrades sharply or requires full retraining per system. This blocks the field's central promise: screening large protein-ligand complexes, extrapolating to novel chemistries, and reusing models across temperatures, states, or unseen systems. Without a measurement standard for out-of-distribution molecular generalization, papers cannot even quantify how far their models actually transfer.

Possible candidate · 3/5 runs14 papers report this75% from 2025+

Molecular Modeling & Drug Design

Scope to testPartly addressed

Benchmarking Cross-System and Cross-Ensemble Generalization in Molecular Generative and Transition Models

Current deep learning models for molecular transitions, ensemble generation, and structure-based drug design are routinely trained and evaluated on isolated systems, fixed thermodynamic conditions, or homogeneous configuration spaces. When deployed on unseen molecular systems, new temperature/pressure regimes, novel pairs of meta-stable states, or shifted configuration spaces (such as moving from isolated ligands to pocket environments), these models require costly de novo retraining. Because prior studies only evaluate models within their narrow training distribution, the extent to which existing architectures can transfer across these varying physical and structural conditions remains unknown.

Weak candidate · 1/3 runs3 papers report this33% from 2025+
Generated automatically, not curated by hand. Automated prior-work checks catch about a third of existing work, so treat this problem as a lead to investigate.