QSAR-Guided Generative Framework for the Discovery of Synthetically Viable Odorants

Tim C. Pearce; Ahmed Ibrahim

Figure 1

Figure 1: Variational Autoencoder framework concept. Abbreviations: CNN, convolutional neural network; GRU, gated recurrent unit; FC, fully connected (dense); KL, Kullback-Leibler divergence; Recon Loss, reconstruction loss (categorical cross-entropy); Prop Loss, property prediction loss (mean squared error, as described in text). The diagram illustrates the flow: input SMILES from ChEMBL are processed by an Encoder (CNN based) mapping to a continuous latent space. Latent vectors

z

are input to both a Decoder (GRU based) for reconstructing SMILES sequences and an Odor Property Prediction head (FC based) predicting odor probability

P(\text{odor})

jointly with two auxiliary regression targets (LogP and boiling point), of which only

P(\text{odor})

enters the property loss. The combined training objective minimizes the Recon Loss, the Prop Loss, and the KL divergence regularization term.

Hyperparameter	Values Evaluated
Latent Space Dimensions ( $D$ )	64	128	196	256	512
GRU Hidden Units	128	488	512	768	1024
Convolutional Growth Factor	1.00	1.10	1.12	1.16	1.25
Learning Rate ( $\eta$ )	$1.0\times 10^{-3}$	$5.0\times 10^{-4}$	3.12 $\times$ 10 -4	$1.0\times 10^{-4}$	$1.0\times 10^{-5}$
Adam Optimizer Decay ( $\beta_{1}$ )	0.900	0.925	0.937	0.950	0.990
Batch Size ( $B$ )	32	64	100	128	256

Table 1: Hyperparameter Optimization Search Space. The optimal values selected for the final VAE-QSAR model are highlighted in bold .

Figure 2: QSAR Odor molecule prediction results. a) Schematic showing the QSAR model pipeline where logistic regression was trained on 1,924 experimentally validated and/or literature reported molecules and subsequently used to label ChEMBL molecules with a target odor probability, b) UMAP visualization of training set in space of molecular descriptors showing the odor probability, c) classifier performance for logistic model, and d) Distribution of predicted probabilities across the training set, and e) residual plots for the logistic regression model.

Figure 3: a) UMAP dimensionality reduction and visualization of the molecular descriptor space showing ~5 x

10^{5}

VAE training set ChEMBL molecules (green) alongside the original set of odor molecules (red) and non-odor molecules (blue) and the same UMAP representation showing the predicted probabilities across all ChEMBL molecules used for VAE training as predicted by the QSAR model (right). Inset histogram for the frequency of odor probabilities in the ChEMBL set used to train the VAE, b) t-SNE dimensionality reduction of 1 x

10^{4}

randomly sampled ChEMBL molecules encoded into the latent space (green) together with validated odor (red) and non-odor molecules (blue), c) Validated odor molecule probabilities as predicted by the QSAR model,

P(\text{qsar})

, compared against VAE odor property prediction,

P(\text{odor})

. Shaded region shows where both models agree on odor molecule prediction (

P(\text{odor})>0.5

and

P(\text{qsar})>0.5

).

Dep. Variable:	odor.class
No. Observations	3230
Model	Logit
Df Residuals	2858
Method	MLE
Df Model	371
Pseudo R 2	0.894

Table 2: Logistic Regression Model Diagnostics

Figure 4: Generated molecules from two example seed odor molecules. a & b) Two example seed molecules (seed is distance 0.0, top left) with sampling of the latent space over increasing distance from the seed in latent space (units of

\sigma_{\phi}(z|X)

). Where available Pubchem catalogued compounds show their corresponding IUPAC name and CID reference, whereas potentially novel compounds show the corresponding SMILES and CID: N/A. SA: synthesis accessibility score. c) traversing the latent space between two odor seed molecules

t=0

is start seed and

t=1

is the end seed in 11 discrete steps. Attempts are the number of probabilistic decode cycles required to obtain a valid structure at that point of the latent space.

Figure 5: Generated molecules’ uniqueness. a) Jaccard similarity, and b) Fréchet ChemNet distance across data-sets.

Novelty Category	Count	% of ring-containing	MW	LogP	log 10 (VP)	In AD (%)	Interpretation
1. Exact Memorization	423	6.3	157.64	2.44	1.58	100.0	Model memorized training data (Overfitting).
2. Odorant Derivatization	3291	48.6	181.18	2.47	1.18	81.2	Optimizing side-chains of known
(QSAR Scaffold)	3291	48.6	181.18	2.47	1.18	81.2	odorant frameworks.
3. Repurposing	258	3.8	158.31	2.23	1.70	50.8	Converting generic bioactive
(ChEMBL Scaffold)	258	3.8	158.31	2.23	1.70	50.8	frameworks into odorants.
4. Validated Scaffold Hop	294	4.3	153.27	2.01	2.61	33.0	True Generalization: Found known odorant

Table 3: Bemis-Murcko Scaffold Classification of Generated Molecules. The analysis distinguishes between memorization, derivatization of known structures, and the discovery of novel core frameworks. Average properties (Molecular Weight, LogP, and

\log_{10}

Vapor Pressure) indicate that even novel scaffolds maintain physicochemical profiles consistent with volatility. Averages are arithmetic means over the molecules of each category;

\log_{10}

(VP) is the mean of per-molecule

\log_{10}(P_{\text{sat}}/\text{Pa})

values, averaged over those molecules carrying a vapor pressure prediction (

n=325

of

423

for category 1; complete coverage for categories 2 to 5). Classification was performed on the ring-containing subset of the canonically de-duplicated generated molecules with

P(\text{qsar})>0.99

(

n=6{,}765

of

18{,}992

; the remaining

12{,}227

are acyclic and have no defined scaffold, see Supplementary Table S1). Category 1 is a molecule-level match; categories 2 to 4 are framework-based and are therefore defined only for ring-containing molecules. Rows 2 to 4 are unaffected by the ring restriction, having no acyclic members. “In AD” is the percentage of each category’s molecules lying inside the applicability domain, that is, within a nearest-neighbor Tanimoto similarity of

0.35

on ECFP4 fingerprints to the QSAR training set. The applicability-domain column is reported, not applied: Table 3 classifies the full ring-containing library, while the

0.35

threshold is applied only to the proposed candidate set (Methods, “Applicability domain”; Results, “Applicability domain of the QSAR model”).

Figure 6: Generated molecules odor probability. a) UMAP representation of seed and generated molecules encoded in the latent space color-coded by odor (inset shows the

P(\text{qsar})

distribution across all generated molecules). b) probability of generated molecule being odorous with distance from the seed (in units of

\sigma_{\phi}(z|X)

) averaged over all generated molecules (thickness shows 95% confidence interval). c) QSAR model predicted odor probabilities of random 1k molecules sampled from ChEMBL, generated molecules set and unique Good Scents (ground truth compounds).

Figure 7: Ablation of the odor prediction head. Both models share an identical architecture, training corpus and training schedule, differing only in the presence of the QSAR property loss (

\gamma\mathcal{L}_{P(\text{odor})}

, Equation 12 ); both were seeded with the same

984

odorant compounds (

P(\text{qsar})>0.9

) and given

100

decode attempts per seed. Distance is normalized by each model’s own mean pairwise inter-seed distance (

2.59

with the head,

1.57

ablated), so that

1.0

(dotted vertical line) corresponds to the typical separation between two seed odorants; without this normalization the

1.65\times

contraction of the ablated latent space would itself produce an apparent difference. (a) Distinct novel molecules per seed as a function of distance; shading gives the 95% confidence interval bootstrapped over seeds. (b) Mean QSAR odor probability of the same compounds, both models scored with the same external logistic-regression model. (c) Zero-perturbation decoding control, each seed’s own latent mean decoded

100

times: valid strings emitted, decodes returning the seed itself, and distinct molecules obtained. (d) Bemis-Murcko classification of the novel compounds (

P(\text{qsar})>0.99

) through the pipeline of Table 3 , restricted to ring-containing molecules. Values are given in the text.

Novelty category	Count	% Gen.	Count	% Gen.
	Full model		Ablated
1. Exact Memorization	127	4.2	58	8.5
2. Odorant Derivatization (QSAR scaffold)	1,441	47.7	393	57.9
3. Repurposing (ChEMBL scaffold)	130	4.3	36	5.3
4. Validated Scaffold Hop (rediscovery)	122	4.0	23	3.4
5. Uncharted Scaffold Hop (new IP)	1,199	39.7	169	24.9

Table 4: Bemis-Murcko scaffold classification of the novel compounds generated by the full model and by the ablated model (no odor prediction head), from an identical seed set (

984

seeds,

P(\text{qsar})>0.9

) and an identical budget of

100

decode attempts per seed. Both sets were classified against the same three reference sets (QSAR training set, ChEMBL, Unique Good Scents) and filtered at

P(\text{qsar})>0.99

, exactly as in Table 3 and restricted to ring-containing molecules, as defined in Methods (“Scaffold classification”).

	Chemical Validity Checks			Geometric	Physical	Structural Alerts (PAINS)		Novelty / Overlap
Dataset	RDKit	OpenBabel	Cross	Bredt	Energy Strain	Clean /	PAINS	Unique	Overlap
	Valid (%)	Valid (%)	Valid (%)	Compliant (%)	Acceptable (%)	Free (%)	Alerts (%)	(%)	(%)
Generated molecules	100.00	100.00	100.00	99.90	99.38	97.74	2.26	93.94	6.06
QSAR Training Set	100.00	100.00	100.00	99.27	97.00	97.51	2.49	95.74	4.26
Unique Good Scents	100.00	100.00	100.00	96.85	99.60	99.24	0.76	99.08	0.92
All Good Scents	-	-	-	97.13	99.40	99.22	0.78	97.45	2.55

Table 5: Combined Validity, Energy, Structural, and Novelty Metric Checks

Figure 8: Physicochemical and ADMET property validation on generated molecules compared against ChEMBL training molecules and unique Good Scents (ground truth compounds) . a-h) Predicted physicochemical properties, i-l) Predicted ADMET properties, NR-AR: Nuclear Receptor – Androgen Receptor; NR-ER: Nuclear Receptor – Estrogen Receptor; AMES - Ames Mutagenicity Test; Skin Reaction. Panels f–h show integer-valued counts as discrete bars giving the relative frequency of each value; the remaining panels show kernel-density violins.

Data-set	% Solved	Avg state	Min state	Max state	State score	Avg synth	Min synth	Max synth	Synth Routes	Avg	Avg	Avg precurs.
		score	score	Score	st. dev.	routes	routes	routes	st. dev	reactions	precursors	in stock
Generated molecules	100.00	0.76	0.05	1.00	0.27	5.30	1.00	25.00	2.71	2.89	2.54	2.11
Unique Good Scents	100.00	0.92	0.05	1.00	0.17	4.10	1.00	16.00	2.31	1.85	1.99	1.71
ChEMBL	100.00	0.90	0.05	1.00	0.15	4.85	1.00	21.00	2.50	2.65	2.67	1.71
All Good Scents	100.00	0.93	0.05	1.00	0.17	3.82	1.00	15.00	2.15	1.61	1.83	1.60

Table 6: Retrosynthesis analysis of data-sets

Figure 9: Synthesis accessibility and quantum chemistry checks. a) UMAP representation of seed and generated molecules encoded in the latent space color-coded by synthesis accessibility (SA – lower score relates to simpler synthesis complexity), b) comparison of total molecule and per atom energy calculations between generated molecules and Good Scents (ground truth molecules).

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