title: Benchmarking and improving artificial intelligence-aided high-throughput virtual screening methods for drug leads
Creators: name: Hao, Yang
Publisher: name: University of Liverpool Repository
Year: 2029

description: The detection and prediction of compound-protein interactions (CPI) are crucial for the discovery of lead compounds in early drug development. In silico methods, such as molecular docking or molecular dynamics simulations based on protein 3D structures, are often rendered ineffective when the protein structure is unknown. While the advent of AlphaFold has enabled the high-accuracy prediction of protein 3D structures, these structures may not always be suitable for modeling interactions between compounds and proteins. Additionally, ligand-based virtual screening (VS) methods are not applicable when a target lacks known active compounds. With the advancement of artificial intelligence (AI), deep learning-based CPI models are emerging as a new paradigm for virtual screening. To circumvent the dependency on structural data, the "sequence-to-drug" concept has been utilized to design and optimize these models, enabling them to learn from existing protein sequence – compound pairs to generalize to new CPI pairs. While these state-of-the-art methods have demonstrated impressive predictive performance on kinase and GPCR datasets, their effectiveness in large-scale virtual screening remains suboptimal, particularly for novel targets. The limited generalization of these advanced methods stems from an inherent issue: deep learning models tend to rely on the positive-to-negative sample ratio of compounds and proteins as shortcuts for prediction, rather than learning the underlying binding mechanisms. Additionally, overrepresented compounds, often resulting from specific assays where batches of similar compounds were tested against the same target, can introduce bias.
Here, we introduced Clustering-based Down-sampling and Putative Negative (CDPN), a protocol for curating a CPI benchmark dataset which can mitigate the biases while enlarging the diversity of the training dataset. By removing overrepresented compounds and using putative negatives to balance targets and compounds with excessively high positive-to-negative ratios, we demonstrated a significant reduction in biased predictions during virtual screening.
This newly constructed debiased dataset enabled us to reassess the deep learning CPI models developed so far. Specifically, we conducted an ablation study to progressively evaluate compound encoders, protein encoders, and interaction modules. A new framework incorporating the latest pre-training methods, KPGT for compound encoders and the Ankh protein language model for proteins, demonstrated significant improvements on both validation sets and virtual screening tasks when targets were unseen during training. However, limitations still exist, particularly in affinity prediction, highlighting the need for future development.
For specific targets with known activity data, ligand-based methods using SVM also demonstrated performance comparable to deep learning models. To mitigate the issue of high false-positive rates in virtual screening due to data sparsity, the enhancement of negative data, along with semi-supervised Positive and Unlabeled (PU) learning, effectively reduces positive contamination among unlabeled compounds. Additionally, ensemble learning via bagging can lower model overfitting. In multi-target drug screening, achieving high recall rates while controlling false positives is essential. Therefore, we developed a novel semi-supervised learning framework, Negative-Augmented PU-bagging (NAPU-bagging) SVM, which shows promise in discovering novel dual-target inhibitors for EGFR-ALK, targeting non-small cell lung cancer, and identifying full agonist candidates for dopamine receptors (DRD1, DRD2, DRD3, DRD4, DRD5) for neurodegenerative disease treatment. These findings contribute to advancements in polypharmacology therapy for complex diseases.
Finally, we developed DeepSEQreen, a freely accessible web server offering a comprehensive suite of state-of-the-art, sequence-based deep learning models for drug hit screening, target protein identification, and interaction pair inference. All CPI models were retrained on our newly developed CDPN dataset, with recommendations provided based on benchmarking results. Users can also access molecular property calculations, enabling richer information for informed decision-making and supporting early-stage drug discovery efforts. 
In summary, this work provides a diverse and unbiased dataset CDPN and an integrated platform DeepSEQreen, offering insights that may inspire more effective applications of sequence-to-drug models in drug discovery.