Molexar-10M Omni is the universal multi-condition model for Molexar, a unified multimodal molecular foundation model for drug design. It starts from fairydance/molexar-10m-base and is supervised fine-tuned to generate Fragment-SELFIES molecules under scalar molecular-property, pharmacophore-fingerprint, protein-sequence, and protein-pocket conditions.
This model corresponds to the Universal Multi-Condition Model described in the Molexar paper.
Parameter counts are unique nn.Parameter counts with tied token-embedding/LM-head weights counted once. The LM component includes the token embeddings, Gemma2-style decoder, final normalization, and tied output head; the total additionally includes condition encoders and the pocket GVP encoder.
Molexar uses a shared sequence template for pretraining, SFT, and inference:
The condition block contains ordered key-token/value-token pairs. During conditional generation, selected <VALUE> token embeddings are replaced in place by encoded condition vectors. This keeps all generation modes on the same autoregressive decoding path and remains compatible with key-value-cache generation.
Supported Conditions
Key
Meaning
Encoding / Range
mol_hac
Heavy atom count
one-hot, 2 to 50
mol_hbdc
Hydrogen-bond donor count
one-hot, 0 to 10
mol_hbac
Hydrogen-bond acceptor count
one-hot, 0 to 22
mol_rotbc
Rotatable bond count
one-hot, 0 to 20
mol_wt
Molecular weight, Da
RBF, 30 to 750, 128 steps
mol_logp
LogP
RBF, -6 to 12, 96 steps
mol_tpsa
Topological polar surface area
RBF, 0 to 200, 96 steps
mol_qed
QED
RBF, 0.3 to 1.0, 64 steps
mol_sas
Synthetic accessibility score
RBF, 1.0 to 5.0, 64 steps
mol_pharma_fp
2D pharmacophore fingerprint
direct vector, 1032 dimensions
prot_seq_esm_emb
Protein sequence embedding
direct vector, 1152 dimensions
prot_poc_gvp_emb
Protein pocket geometry embedding
GVP/pocket vector, 256 dimensions
Protein sequence conditioning uses mean-pooled ESMC-600M final embeddings in the paper. Pocket conditioning processes no-hydrogen pocket PDB structures with a 25 Angstrom radius, a maximum of 425 atoms, and a directed 8-nearest-neighbor atom graph.
Installation
Install Molexar and Fragment-SELFIES before loading the model:
Install the runtime dependencies listed in the Molexar repository documentation. Fragment-SELFIES is required to convert generated Fragment-SELFIES strings to SMILES. Protein-sequence conditioning also requires the auxiliary ESM embedding environment described by the Molexar repository.
Omni also supports fragment-constrained generation with active conditions by combining condition flags with --generation_task and --start_smiles or --start_string. Supported generation tasks are de_novo, motif_extension, scaffold_decoration, linker_design, scaffold_morphing, and superstructure.
Training
Molexar-10M Omni was initialized from Molexar-10M Base and trained with universal multi-condition SFT. The SFT objective masks the prefix through <MOL> and applies loss to the molecular continuation and closing tokens conditioned on the prompt and injected values.
The SFT data combines molecule-context and target-context samples. Molecule-context samples use the UniChem-derived Fragment-SELFIES corpus with nine scalar properties and a 2D pharmacophore fingerprint. Target-context samples use protein-ligand pairs from SAIR and the PLINDER training set, with protein-sequence ESM embeddings and processed pocket structures. The Molexar paper reports removing target-context training pairs whose protein sequence had more than 30% identity to any CrossDocked2020 test protein; after filtering, the target-context pool contains 573,463 SAIR pair records and 21,770 PLINDER training-set pair records.
Main training settings from the release script and paper:
Setting
Value
Objective
Universal multi-condition supervised fine-tuning
Sequence length
256
Epochs
5
Batch size
1000
Learning rate
2e-4
Warmup steps
2000
Molecule:target sample ratio
4:1
Molecule-side active conditions
1, 2, or 3 conditions with probabilities 0.6, 0.3, 0.1
Pharmacophore oversampling probability
0.5
Mixed precision
bfloat16
Distributed training
Full-shard FSDP on 8 H800 GPUs
Evaluation Highlights
The Molexar paper reports that the SFT model follows single-, dual-, and triple-property instructions and supports pharmacophore, protein-sequence, and pocket-geometry conditioning.
On MolGenBench, the paper reports high chemical-filter pass rates, strong active-molecule and scaffold recovery in de novo generation across protein targets, and favorable hit-to-lead potency when conditioning jointly on pocket and reference-ligand pharmacophore.
Intended Use
This model is intended for research use in molecular generation workflows, including:
Fragment-constrained generation with optional active conditions.
Generated molecules should be treated as computational hypotheses. They require independent chemical-safety filtering, synthetic feasibility assessment, intellectual-property and dual-use review where relevant, expert medicinal-chemistry assessment, and experimental validation before downstream use.
Limitations
The model was trained on filtered drug-like chemistry; rare, contradictory, or out-of-distribution condition combinations may be followed less reliably.
Docking, pharmacophore, property, or sequence/pocket scores are not evidence of biological activity, safety, or clinical utility.
Protein-sequence and pocket conditioning depend on preprocessing quality, including ESM embeddings and pocket structure preparation.
Fragment-SELFIES decoding improves validity but does not guarantee synthetic accessibility, biological activity, safety, or developability.
The released tokenizer does not include the iodine token [I]; use bromine substitution in start constraints when necessary, as documented by the Molexar inference script.
Stereochemical and explicit 3D output control are outside the scope of this model.
License
This model is released under the MIT License.
Citation
If you use this model, please cite Molexar and Fragment-SELFIES:
bibtex
1@misc{lin2026molexar,
2 title = {Molexar: A Unified Multimodal Molecular Foundation Model for Drug Design},
3 author = {Lin, Haoyu and Liao, Yiyan and Pan, Jinmei and Ling, Xinliao and Lai, Luhua and Pei, Jianfeng},
4 year = {2026},
5 url = {https://molexar.com}
6}
