MoLRSI: A Recursive Self-Improving AI agent for Molecular Discovery

Yikun Zhang; Xiwei Cheng; Mingze Yuan; Yu Zhang; Brian Shim; Aarti Krishnan; Daniel Rosen; Kunyang Sun

cs.AIOct 29, 2026

MoLRSI: A Recursive Self-Improving AI agent for Molecular Discovery

Authors: Yikun Zhang, Xiwei Cheng, Mingze Yuan, Yu Zhang, Brian Shim, Aarti Krishnan, Daniel Rosen, Kunyang Sun,

Organizations: Northeastern University, Boston, MA, USA · Broad Institute of MIT and Harvard, Cambridge, MA, USA · Harvard University, Cambridge, MA, USA · Wyss Institute for Biologically Inspired Engineering, Harvard University, Boston, MA, USA · Massachusetts Institute of Technology, Cambridge, MA, USA · Brigham and Women’s Hospital and Dana-Farber Cancer Institute, Boston, MA, USA · University of California, Berkeley, Berkeley, CA, USA · University of Toronto, Toronto, ON, Canada · The Ohio State University, Columbus, OH, USA · University of Washington, Seattle, WA, USA · Howard Hughes Medical Institute, Cambridge, MA, USA · Yale University, New Haven, CT, USA · Microsoft Research New England, Cambridge, MA, USA · Massachusetts General Hospital, Boston, MA, USA

Abstract

Molecular discovery requires navigating vast combinatorial search spaces while balancing competing biological objectives. AI agents can automate expert-defined molecular design protocols, but automation alone does not address how to optimize workflows for new tasks or improve their development through experience. Here we introduce MoLRSI, an AI agent that combines systematic optimization of workflows and model architectures with persistent cross-task memory to recursively improve its search policy for molecular discovery. Workflow performance, refinement outcomes, and execution feedback guide subsequent searches, allowing the agent to improve both its workflows and its strategy for developing them. We evaluate MoLRSI in two challenging molecular discovery settings that require balancing competing properties. In antibiotic discovery, MoLRSI developed a screening workflow balancing antibacterial activity, cytotoxicity and structural novelty, achieving an experimental hit rate of 15.9%, compared with 8.7% for a previous expert-led workflow using the same training data and screening library. In de novo peptide-MHC binder design, MoLRSI developed workflows that jointly consider target binding and specificity, producing eight experimentally validated hits against MART-1 and SARS-CoV-1 pMHCs with preferential binding over tested off-targets while Claude Science completely failed. These findings demonstrate how experience-guided workflow optimization can address competing discovery objectives while improving the search strategies used in subsequent campaigns.

Figures & tables

Figure 1: MolRSI motivation and architecture. (a) Molecular discovery involves a combinatorial space of decisions across preparation, generation and prioritization, illustrated for de novo protein binder design. Nodes represent options at each stage. The orange path traces one possible workflow. (b) Scientists accumulate lessons from experimental outcomes across projects, learning to avoid unproductive choices and focus exploration on promising regions. (c) MolRSI couples systematic workflow search with persistent cross-task memory. The Explore agent retrieves relevant resources from scientific literature and code repositories, then converts them into reusable, executable skills through implementation and validation. Workflow evaluations update solution memory (workflows and performance), refinement memory (modifications and their effects) and execution memory (execution strategies and repairs). Accumulated experience guides subsequent workflow selection, refinement and execution, recursively improving the effective search policy from $\pi_{t}$ to $\pi_{t+1}$ . Colored paths indicate evaluated workflows, faded elements denote deprioritized choices, and orange-outlined nodes represent new local options. Projects, outcomes and memory entries are illustrative.

Figure 2: Computational evaluation of MolRSI for antibiotic discovery. (a) Test AUPRC and rankings of MolRSI without RSI and baseline agents across six antibacterial tasks. (b) Resource library. (c) Experience from 25 source tasks, excluding the antibacterial targets. (d) Search trajectories over 20 iterations. (e) Search with and without RSI and zero-search transfer, which retains model fitting and validation. (f) Zero-search transfer performance versus memory-pool size. The line shows test AUPRC averaged analytically across the six held-out antibacterial tasks and all possible subsets of each size from the 25-task memory pool. Darker and lighter shading indicate the interquartile range (IQR; 25th–75th percentiles) and the 5th–95th percentile range, respectively, across 2,000 sampled memory subsets at each pool size. In (a,d,e), means and $\pm 1$ sample SD are shown across five data splits. Circles in (a) denote individual splits.

Figure 3: Virtual screening and experimental validation with MolRSI. (a) Screening workflow yielding 107 tested compounds; cytotoxicity-filter exceptions are detailed in Section S3.4 . (b) t-SNE projection [ ? ] of training and tested compounds; stars indicate hits. (c) Activity-score ranking of tested compounds. (d) MSSA and MRSA MICs and HepG2, HSkMC and IMR-90 $\mathrm{CC}_{50}$ values for two selected hits, C3 and C32; dots show individual MIC measurements. (e) Hit rates for 107 MolRSI -selected and 241 expert-selected compounds [ ? ], with 95% Wilson confidence intervals. (f) AUROC and AUPRC on the shared 271-compound held-out S. aureus test set. (g) Chemical structures and dose–response curves of C3 and C32. Symbols show responses; lines indicate descriptive logistic fits. Two response series are shown per curve. Hits are defined by an MSSA MIC of 32 $\mu$ g/mL or lower.

Figure 4: Benchmarking and experience-guided optimization of pMHC binder-design workflows. (a) Enrichment Factor (EF) at the top-ranked 1% and 2% cutoffs (EF@1% and EF@2%) for MolRSI and four agent baselines across 14 miniprotein-binder tasks. Heatmaps show individual tasks; bars show the unweighted macro average across tasks. (b) Protein resource library organized into input preparation, generation, sequence design and assessment, structure prediction, scoring and score combination. (c) EF@1% and EF@2% on the held-out MART-1 pMHC target for MolRSI with and without RSI and four agent baselines. (d) Best-so-far search objective, defined as the mean ranking score among the top 5% of candidates, during MART-1 and SARS-CoV-1 design-workflow optimization. (e) On-target versus off-target scores for selected MART-1 and SARS-CoV-1 designs from MolRSI and Claude Science, evaluated with the MolRSI ranking score (left) and Claude Science score (right).

Figure 5: Prospective experimental validation of pMHC binder designs. Upper and lower rows in a–c show MART-1 and SARS-CoV-1, respectively. (a) On-target log2FC and its difference from the maximum available off-target log2FC. Dashed lines mark hit thresholds ( $x>4$ , $y>0$ ); legend values indicate hits/evaluable designs. (b) UMAP [ ? ] of predicted minibinder structures based on TM-align [ ? ] similarity; stars mark experimental hits. (c) Designs ranked by MolRSI score, with hits highlighted in green. (d) Predicted complexes of sars1_0101 and mart1_0014. Minibinders, MHC and peptides are blue, orange and pink, respectively; yellow dashed lines indicate candidate polar contacts.

Appendix figures & tables9 assets

Supplementary material from the paper’s appendix.

Appendix

Component	Information provided
Shared index	Skill names, categories, purposes and links to the corresponding instructions.
Skill instructions	Resource description, intended use, installation, supported operations and usage examples.
Implementation	Available source code, model definitions, dependencies and supporting scripts.
Source information	Scientific references, repository location, revision and package information.
Interfaces and examples	Available operations and examples of feature extraction, model fitting or inference, as appropriate to the resource.
Validation information	Validation status, checks performed and their outcomes.

Appendix

Table S1: Contents of the resource library. The shared index identifies available skills. Each skill combines instructions with the implementation, source information and validation results available for that resource. The supporting material depends on the resource.

Category	Dataset	Endpoint	Size	Metric
Absorption	Caco-2	Intestinal permeability in a Caco-2 assay	906	MAE
Absorption	HIA	Human intestinal absorption	578	AUROC
Absorption	Pgp	P-glycoprotein inhibition	1,212	AUROC
Absorption	Bioavailability	Oral bioavailability	640	AUROC
Absorption	Lipophilicity	Octanol–water distribution coefficient	4,200	MAE
Absorption	Solubility	Aqueous solubility	9,982	MAE

Appendix

Table S3: The 25 source tasks used for memory accumulation. The first 22 tasks are from the TDC ADMET benchmark, and the remaining three are mammalian cytotoxicity tasks. TDC sizes are the reported benchmark sizes; these tasks used the official benchmark partitions.

Dataset	Seed	Total	Positive	Total	Positive	Total	Positive
		Training		Validation		Test
S. aureus	1	31,512	340	3,939	48	3,939	82
	2	31,512	347	3,939	48	3,939	75
	3	31,512	343	3,939	55	3,939	72
	4	31,512	335	3,939	59	3,939	76
	5	31,512	337	3,939	54	3,939	79

Appendix

Table S5: Training, validation and test sizes for the nine datasets. Total gives the number of samples and Positive gives the number with a positive label. Negative counts equal Total minus Positive. Seeds are grouped only when all partition sizes and positive counts are identical; grouped seeds retain different sample assignments.

Target	Data source	Source designs	Retained positives	Sampled negatives
EGFR	Cao et al. [ ? ]	99,803	15	150
FGFR2	Cao et al. [ ? ]	59,999	601	6,010
H3	Cao et al. [ ? ]	59,941	49	490
IL-7R $\alpha$	Cao et al. [ ? ]	14,999	22	220
InsulinR	Cao et al. [ ? ]	59,973	256	2,560
PDGFR	Cao et al. [ ? ]	99,992	284	2,840

Appendix

Table S9: Composition of the 14-task miniprotein-binder benchmark. Each row identifies the held-out test target for one task; workflow search and selection used the other 13 targets. Labels use the source-provided binary binding endpoint at 4,000 nM. All positives were retained, and up to ten negatives per positive were sampled within each target.

Antigen	Peptide	Role	Candidates	Positives
gp100	YLEPGPVTA	Search	2,611	14
gp100	IMDQVPFSV	Search	3,271	4
MART-1	ELAGIGILTV	Test	3,845	27

Appendix

Table S10: Dataset roles within the single pMHC binder-design task. Both complete gp100 collections were used for workflow search; MART-1 was reserved for test evaluation.

Method	EF@1 %	EF@2 %	EF@5 %	EF@10 %	AUPRC
FGFR2
MolRSI	5.58	4.47	3.16	2.61	0.2041
Claude Science	7.06	5.87	4.22	3.22	0.2871
Biomni	6.07	5.21	3.79	2.69	0.2212
STELLA	4.93	4.71	3.82	2.82	0.2193
Codex	4.27	3.14	2.69	2.09	0.1554

Appendix

Table S11: Target-level results for the miniprotein-binder benchmark. Each target was held out during the corresponding agent workflow search; no cross-task memory was used. Fixed objectives were evaluated on the same candidates. The target-specific rows report EF at four cutoffs and AUPRC.

Method	EF@1 %	EF@2 %	EF@5 %	EF@10 %	AUPRC
MolRSI	3.65	5.55	2.21	2.22	0.0172
Claude Science	0.00	1.85	0.74	1.48	0.0089
Biomni	0.00	1.85	0.74	1.11	0.0087
STELLA	0.00	1.85	1.48	1.48	0.0095
Codex	0.00	0.00	1.48	0.74	0.0088
BoltzGen	0.00	1.85	0.74	0.74	0.0067

Appendix

Table S12: Held-out pMHC test performance. Agent workflows were selected using gp100 data; fixed expert-defined objectives used the same test candidates. EF values are shown at four cutoffs, alongside AUPRC. Values retain the precision of the source export.

Condition	EF@1%	EF@2%	EF@5%	EF@10%	AUPRC
With RSI	3.65	5.55	2.21	2.22	0.0172
Without RSI	0.00	0.00	1.48	1.48	0.0125

Appendix

Table S13: Effect of source task memory on MART-1 test performance. With and without RSI denote searches with and without prior miniprotein-binder experience.

Input	MART-1	SARS-CoV-1
HLA allele	HLA-A*02:01	HLA-A*02:01
On-target peptide	ELAGIGILTV	GLMWLSYFV
Off-target peptide	GLMGAGIAQV	GLIFSSYFV
Structure provenance	PDB 5NHT	PDB 3I6G
Source PDB chains (HLA heavy chain / $\beta_{2}$ m / peptide)	H/L/P	A/B/C
Binder-length range	80–120 residues	80–120 residues

Appendix

Table S14: Inputs and target-specific constraints for the two prospective pMHC binder-design tasks. Source chains give the HLA heavy-chain,

\beta_{2}

-microglobulin, and peptide chain identifiers in the corresponding PDB entry. Peptide positions are one-based. Numbers in parentheses are zero-based indices in the concatenated target sequence.

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