Chengwei Ai

De novo drug design is crucial in advancing drug discovery, which aims to generate new drugs with specific pharmacological properties. Recently, deep generative models have achieved inspiring progress in generating drug-like compounds. However, the models prioritize a single target drug generation for pharmacological intervention, neglecting the complicated inherent mechanisms of diseases, and influenced by multiple factors. Consequently, developing novel multi-target drugs that simultaneously target specific targets can enhance anti-tumor efficacy and address issues related to resistance mechanisms. To address this issue and inspired by Generative Pre-trained Transformers (GPT) models, we propose an upgraded GPT model with generative adversarial imitation learning for multi-target molecular generation called MTMol-GPT. The multi-target molecular generator employs a dual discriminator model using the Inverse Reinforcement Learning (IRL) method for a concurrently multi-target molecular generation. Extensive results show that MTMol-GPT generates various valid, novel, and effective multi-target molecules for various complex diseases, demonstrating robustness and generalization capability. In addition, molecular docking and pharmacophore mapping experiments demonstrate the drug-likeness properties and effectiveness of generated molecules potentially improve neuropsychiatric interventions. Furthermore, our model’s generalizability is exemplified by a case study focusing on the multi-targeted drug design for breast cancer. As a broadly applicable solution for multiple targets, MTMol-GPT provides new insight into future directions to enhance potential complex disease therapeutics by generating high-quality multi-target molecules in drug discovery. Advancing drug discovery with the vast scale of possible structures of drug-like compounds (between 10²³ and 10⁶⁰) relies on Artificial Intelligence (AI). The process of generating SMILES has been accomplished by using language models like the Generative Pre-trained Transformer (GPT) model. In the meantime, regulating multiple targets to achieve the desired physiological responses is booming for treating complicated diseases. However, the recent deep generative models focus on single-target drug generation, and single-target drugs have limitations, such as requiring higher doses and causing mutations. For example, activation of Src kinase in certain breast cancer cells leads to EGFR phosphorylation and downstream effects. Consequently, designing multi-target molecules against multi-target simultaneously is highly required. Thus, we have proposed an upgraded GPT model MTMol-GPT with generative adversarial imitation learning for generating high-quality multiple targets drug-like compounds. For example, MTMol-GPT generates novel drugs targeting protein pairs such as DRD2 and HTR1A, EGFR and Src. As a broadly applicable tool, MTMol-GPT provides a rapid and accurate method for advancing drug discovery, ensuring the efficient generation of high-quality multi-target drugs.

Detecting potential associations between drugs and diseases plays an indispensable role in drug development, which has also become a research hotspot in recent years. Compared with traditional methods, some computational approaches have the advantages of fast speed and low cost, which greatly accelerate the progress of predicting the drug-disease association. In this study, we propose a novel similarity-based method of low-rank matrix decomposition based on multi-graph regularization. On the basis of low-rank matrix factorization with $L_{2}$ regularization, the multi-graph regularization constraint is constructed by combining a variety of similarity matrices from drugs and diseases respectively. In the experiments, we analyze the difference in the combination of different similarities, resulting that combining all the similarity information on drug space is unnecessary, and only a part of the similarity information can achieve the desired performance. Then our method is compared with other existing models on three data sets (Fdataset, Cdataset and LRSSLdataset) and have a good advantage in the evaluation measurement of AUPR. Besides, a case study experiment is conducted and showing that the superior ability for predicting the potential disease-related drugs of our model. Finally, we compare our model with some methods on six real world datasets, and our model has a good performance in detecting real world data.

Identifying DNA N4-methylcytosine (4mC) sites is of great significance in biological research, such as chromatin structure, DNA stability, DNA-protein interaction and controlling gene expression. However, the traditional sequencing technology to identify 4mC sites is very time-consuming. In order to detect 4mC sites, we develop a multi-view learning method for achieving more effectively via merging multiple feature spaces. Furthermore, we think about whether the multi-view learning method can improve the across species classification ability by fusing data of multiple species. In our study, we propose a multi-view Laplacian kernel sparse representation-based classifier, called MvLapKSRC-HSIC. First, we make use of three feature extraction methods (PSTNP, NCP, DPP) to extract the DNA sequence features. MvLapKSRC-HSIC uses a kernel sparse representation-based classifier with graph regularization. In order to maintain the independence between various views, we add a multi-view regularization term constructed by Hilbert-Schmidt independence criterion (HSIC). In the experiments, MvLapKSRC-HSIC is applied on six datasets, so as to compare with other popular methods in single species and cross-species experiments. All experimental results show that MvLapKSRC-HSIC is superior to other outstanding methods on both single species and cross-species. Importantly, MvLapKSRC-HSIC can identify a series of potential DNA 4mC sites, which have not yet been experimentally evaluate on multiple species and merit further research.