Singapore

Fragment-based drug discovery (FBDD) leverages small molecular fragments as starting points for drug develop- ment. While SMILES is widely used in cheminformat- ics, its syntax introduces parsing challenges that can af- fect model performance. DeepSMILES, a postfix variant of SMILES, addresses these issues by eliminating paired ring closures and balanced parentheses. In this work, we intro- duce DeepBERTa, a ChemBERTa-based architecture trained on DeepSMILES, and apply it in a downstream FBDD task using a dataset of approximately 34,000 molecule–fragment pairs derived from FDA-approved drugs, with fragments se- lected by docking against the mTOR kinase. We ran both SMILES and DeepSMILES-based models in parallel and show that 1 a hybrid approach, which selects the best predic- tion from either model, raises the mean Tanimoto similarity from 0.290 (SMILES) to 0.347, with DeepSMILES providing the better prediction in 13% of the cases.

AAAI 2026

A Hybrid SMILES-DeepSMILES Transformer Approach Improving Fragment Prediction in Fragment-Based Drug Discovery with DeepBERTa

tanimto similarity

deepsmiles

smiles

fragment-based drug discovery

transformers

## Welcome!
"I am excited to welcome you to this year’s edition of the Conference on Empirical Methods in Natural Language Processing! Importantly, it marks the 30th edition of EMNLP. With over 8,000 submissions, more than 3,000 accepted papers, and thousands of attendees, we have come a long way from that first
workshop, which had 14 accepted papers. As the field looks ahead, Suzhou is the fitting location for celebrating this milestone: rooted in a long literary tradition, yet modern and forward-looking, and home to a large share of the NLP community." 

*Message from the General Chair, Dirk Hovy*

EMNLP 2025

Celebrate 30 Years of EMNLP! 
EMNLP 2025 will be held in Suzhou, China from November 5th to November 9th, 2025.

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We are pleased to announce the Fortieth AAAI Conference on Artificial Intelligence (AAAI-26), which will be held in Singapore EXPO from January 20 to January 27, 2026.

Protein flexibility and induced-fit effects are critical but often overlooked aspects of structure-based drug design. Most existing generative models treat proteins as rigid scaffolds, limiting their ability to predict realistic binding geometries. We present Co-Diffuse, a novel framework that jointly generates ligand molecules and their corresponding protein binding pocket conformations using SE(3)-equivariant diffusion models. By explicitly modeling the mutual adaptation between proteins and ligands, Co-Diffuse captures the dynamic nature of molecular recognition. Comprehensive evaluations on PDBbind, CrossDocked2020, and cryptic site benchmarks demonstrate that Co-Diffuse achieves an average RMSD improvement of 1.8 Å over leading docking baselines and yields more accurate binding affinity predictions. Ablation studies highlight the importance of joint generation and physics-informed constraints. Co-Diffuse represents a significant step toward dynamic, physically grounded generative modeling for structure-based drug design.



Co-Diffuse: Generative Co-Design of Protein–Ligand Interactions via 3D Equivariant Diffusion Models with Induced-Fit Awareness

SynergyIF: Multimodal Intermediate Fusion for Deep Tensor Completion in Anticancer Drug Synergy Prediction

Parkinson’s disease (PD) drug discovery is uncertainty- and constraint-laden: candidates must match disease mechanisms (e.g., GBA1, 
-synuclein), cross the BBB, and meet developability/novelty requirements amid fragmented evidence. We present PD Brain, a disease-specialized, continuously operating platform that turns natural-language intents into executable end-to-end workflows. PD Brain is built on a PD Deep Knowledge Base (DKB) comprising: (i) an Evidence KG of PD facts, (ii) an Understanding Graph that contracts facts into mechanism motifs (Drug
Target
Pathway
Cell-type/Region
Phenotype), and (iii) a Reasoning Graph that scores causal paths under CNS predicates (BBB transport, SNc expression, exposure thresholds) with calibrated uncertainty. The system \emph{plans, auto-codes, executes, and self-updates} the pipeline.

In a GBA1-focused study, PD Brain expanded an Ambroxol seed to 
1,100 analogs, used a multi-agent MoA debate to triage to 
500, applied variant-aware docking (GCase WT and PD-linked mutants) and BBB-aware ADMET to 64, and shortlisted 5 novel leads. A representative candidate Cpd-1 (SMILES: C1C(=O)CCC(CC1NCC2=C(C(=CC(=C2)Br)Br)N)O) docked to GCase at 
kcal/mol with predicted brain penetration, 
90% human intestinal absorption, 
7.7 h plasma half-life, and moderate toxicity risk; other shortlist molecules exceeded it on specific criteria.

Key contributions: a PD-vertical planner (Plan), automatic tool selection and code synthesis (Design), a provenance-first OS for robust execution (Execute), and continuous DKB-driven updates with drift/contradiction handling (Evolve). We discuss implications for disease-focused automated discovery, including oversight, IP awareness, and continual learning.

Parkinson’s on Purpose: A Disease-Vertical Agentic Pipeline from Literature to Leads

Accurate prediction of patient-specific drug responses to new therapeutic compounds is essential for advancing personalized drug discovery and development. However, limited patient-derived datasets and biological heterogeneity between preclinical and clinical data constrain predictive models generalizability. Existing methods that utilize cell-line drug screens to infer patient outcomes often struggle with distributional shifts and lack biological context.

Here, we have presented DCODE (Domain-aligned Context-aware multimODal attEntion autoencoder), a novel framework that learns domain-consistent molecular representations by aligning latent spaces between preclinical and clinical data. This alignment mitigates distributional shifts while preserving clinically relevant biological signals. A gene–drug interaction attention module further models nonlinear treatment dependencies, allowing DCODE to disentangle the molecular determinants of drug response.

Extensive comparative analyses demonstrated that DCODE outperformed state-of-the-art models, including CODE-AE, in predictive accuracy and robustness. When evaluated on independent clinical datasets covering five drugs for 90808 patients, DCODE achieved over 70% accuracy in predicting responses to cisplatin, sorafenib, and temozolomide. Our results are consistent with existing clinical observations, highlighting its potential for developing personalized therapy and drug response biomarkers.

A domain-aligned multimodal attention autoencoder for predicting personalized clinical drug response from cell-line drug screens

The utility of large language models (LLMs) for reviewing clinical trial protocols—focusing on statistical analysis plans (SAPs) and pharmacokinetics–pharmacodynamics (PK–PD)—was evaluated using a testbed of 15 trials spanning small molecules, biologics, and global public-health studies. GPT-4o, prompted with a regulatory-expert persona, was tasked to extract study-design attributes, surface relevant regulatory guidance, and produce structured SAP and PK–PD evaluations. Outputs were benchmarked against source protocols/registries and graded post hoc by ChatGPT and Grok using a predefined rubric. Disease, interventions/comparators, and trial sample size were correctly identified for 15/15, 15/15, and 14/15 trials, respectively; overall protocol-based correctness was 95% (80% versus ClinicalTrials.gov actuals reflecting realized enrollments). Frequently surfaced guidance included ICH/FDA E9 for SAPs and FDA Population Pharmacokinetics for PK–PD. SAP summaries were generally coherent, with occasional context errors (e.g., outdated endpoint adjudication, sidedness of tests, incomplete multiplicity handling). PK–PD objectives/measures were correctly captured in 8/9 protocols and analysis methods in 7/8, including dose-proportionality reasoning for a Phase I example. Cross-model grading showed similar judgments on primary-outcome accuracy, statistical-method correctness, and E9 alignment, with divergence in clinical-interpretability scores. Findings support an LLM-assisted, human-in-the-loop workflow to triage and standardize protocol reviews at scale, while highlighting needs for version awareness, explicit multiplicity control, and more precise guidance retrieval in complex or adaptive designs.

Large Language Models for Clinical Trial Protocol Assessments

The Ribonucleic Acid (RNA) inverse folding problem, designing nucleotide sequences that fold into specific tertiary structures, is a fundamental computational biology problem with important applications in synthetic biology and bioengineering. The design of complex three-dimensional RNA architectures remains computationally demanding and mostly unresolved, as most existing approaches focus on secondary structures. In order to address tertiary RNA inverse folding, we present BeeRNA, a bio-inspired method that employs the Artificial Bee Colony (ABC) optimization algorithm. Our approach combines base-pair distance filtering with RMSD-based structural assessment using RhoFold for structure prediction, resulting in a two-stage fitness evaluation strategy. To guarantee biologically plausible sequences with balanced GC content, the algorithm takes thermodynamic constraints and adaptive mutation rates into consideration. In this work, we focus primarily on short and medium-length RNAs (
100 nucleotides), a biologically significant regime that includes microRNAs (miRNAs), aptamers, and ribozymes, where BeeRNA achieves high structural fidelity with practical CPU runtimes. The lightweight, training-free implementation will be publicly released for reproducibility, offering a promising bio-inspired approach for RNA design in therapeutics and biotechnology.

BeeRNA: tertiary structure-based RNA inverse folding using Artificial Bee Colony

Antibodies recognize antigens via complementary and structurally dependent mechanisms. Therefore, inclusion of antibody inputs is crucial for accurate epitope prediction. Given limited availability of antibody-antigen complex structures, any epitope prediction model will require minimal yet sufficient antibody inputs to ensure precise epitope identification. To address this need, we introduce Epi4Ab, an antibody-specific epitope prediction model that focuses on identifying unique in-contact antigen residues for a given antibody. Epi4Ab requires minimal antibody inputs, specifically VH/VL families and complementarity-determining region sequences. This work has been published in mAbs with doi:10.1080/19420862.2025.2531227.

Epi4Ab: A data-driven prediction model of conformational epitopes for specific antibody VH/VL families and CDRs sequences

Alzheimer’s disease (AD) is a widely studied neurodegenerative disorder, and researchers have endeavored to identify target genes for drug development. However, most existing approaches for target identification rely on correlation-based signals, which often fail to distinguish causal drivers from correlated markers. We present a practical causal discovery framework for gene-level target identification that learns local causal neighborhoods around key phenotypes from single-nucleus RNA-seq data. It integrates AD domain knowledge to guide structure learning and employs stability selection for robust ranking under sampling variability. Leveraging the genetically diverse AD-BXD mouse panel, our method achieves competitive performance against baselines in empirical evaluations and yields candidates consistent with established biological evidence.

Utilizing Local Causal Structure Learning in Target Gene Identification for Alzheimer’s Disease

We introduce Temporal Eigenstate Networks (TEN), a novel architecture for sequence mod eling that achieves O(T) complexity compared to the O(T2) complexity of transformer attention mechanisms, where T is the sequence length. TEN operates by decomposing temporal dynam ics into learned eigenstate superpositions that evolve through complex-valued phase rotations, enabling efficient capture of both short and long-range dependencies. Our approach combines insights from spectral theory, state-space models, and quantum mechanics to create a mathe matically principled framework that eliminates the attention bottleneck while maintaining or exceeding transformer performance. On benchmark tasks, TEN achieves 3-28× speedup over transformers on sequences of length 512-8192 while using 120× less memory, with competitive or superior accuracy on language modeling, long-range reasoning, and time-series prediction. We provide theoretical analysis proving universal approximation capabilities, stability guaran tees, and efficient gradient flow. TEN opens a new direction for scalable sequence modeling that is particularly promising for extremely long sequences, edge deployment, and AGI systems requiring efficient temporal reasoning

Temporal Eigenstate Networks: Linear-Complexity Sequence Modeling via Spectral Decomposition

Reliable, informative, and individual uncertainty quantification (UQ) remains missing in applied machine learning (ML). This hinders the effective application of AI/ML to risk-sensitive drug discovery pipeline. Most methods either fail to provide coverage on new data, inflate intervals so broadly that they are not actionable, or assign uncertainties that do not track actual error, especially under a distribution shift. Protein-ligand binding affinity prediction is especially challenging as assay noise is heterogeneous, chemical space is biased and large, and practical evaluations routinely involve distribution shift. In this work, we introduce a novel uncertainty quantification method, 
Trustworthy Expert Split-conformal with Scaled Estimation for Efficient Reliable Adaptive intervals (TESSERA), that provides per-sample uncertainty with reliable coverage guarantee, informative and adaptive prediction interval widths that track the absolute error. We evaluate on protein–ligand binding affinity prediction under both independent and identically distributed (i.i.d.) and scaffold-based out-of-distribution (OOD) splits, comparing against strong UQ baselines. TESSERA attains near-nominal coverage while maintaining strong efficiency and adaptivity. The intervals expand naturally in regions where data are sparse or uncertainty is high, and contract in well-sampled regions where predictions are reliable, keeping overall coverage consistent across the domain. This adaptivity ensures that uncertainty tracks model error closely, making the predictions actionable. TESSERA produces uncertainty estimates that are simultaneously well-calibrated, efficient, and responsive to data difficulty. These are crucial requirements for trustworthy decision-making in drug discovery.

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Co-Diffuse: Generative Co-Design of Protein–Ligand Interactions via 3D Equivariant Diffusion Models with Induced-Fit Awareness

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Improving Chemical Understanding of LLMs via SMILES Parsing

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