5 papers
Breaking the Bottlenecks: Scalable Diffusion Models for 3D Molecular Generation
Adrita Das, Peiran Jiang, Dantong Zhu +2
Diffusion models have emerged as a powerful class of generative models for molecular design, capable of capturing complex structural distributions and achieving high fidelity in 3D…
Learning from B Cell Evolution: Adaptive Multi-Expert Diffusion for Antibody Design via Online Optimization
Hanqi Feng, Peng Qiu, Mengchun Zhang +4
Recent advances in diffusion models have shown remarkable potential for antibody design, yet existing approaches apply uniform generation strategies that cannot adapt to each antig…
AmpLyze: A Deep Learning Model for Predicting the Hemolytic Concentration
Peng Qiu, Hanqi Feng, Meng-Chun Zhang +1
Red-blood-cell lysis (HC50) is the principal safety barrier for antimicrobial-peptide (AMP) therapeutics, yet existing models only say "toxic" or "non-toxic." AmpLyze closes this g…
Pharmacophore-Conditioned Diffusion Model for Ligand-Based De Novo Drug Design
Amira Alakhdar, Barnabas Poczos, Newell Washburn
Developing bioactive molecules remains a central, time- and cost-heavy challenge in drug discovery, particularly for novel targets lacking structural or functional data. Pharmacoph…
Controllable Text Generation in the Instruction-Tuning Era
Dhananjay Ashok, Barnabas Poczos
While most research on controllable text generation has focused on steering base Language Models, the emerging instruction-tuning and prompting paradigm offers an alternate approac…