Molecule docking has been regarded as a routine tool for drug discovery, but its accuracy highly depends on the reliability of scoring functions (SFs). With the rapid development of machine learning (ML) techniques, ML-based SFs have gradually emerged as a promising alternative for protein-ligand binding affinity prediction and virtual screening, and most of them have shown significantly better performance than a wide range of classical SFs. Emergence of more data-hungry deep learning (DL) approaches in recent years further fascinates the exploitation of more accurate SFs. Here, we summarize the progress of traditional ML-based SFs in the last few years and provide insights into recently developed DL-based SFs. We believe that the continuous improvement in ML-based SFs can surely guide the early-stage drug design and accelerate the discovery of new drugs. This article is categorized under:Computer and Information Science > Chemoinformaticsdeep learning, machine learning, molecular docking, scoring function, structure-based drug design | INTRODUCTIONTraditional drug discovery largely relies on the application of high-throughput screening, an experimental technique with acceptable performance but high cost and low efficiency. 1 With the rapid development of computational chemistry and computer technology, computer-aided drug design (CADD) has gradually emerged as a powerful technique in the design and development of new drug candidates in the past three decades. 2 Virtual screening (VS), an important branch of CADD, can enrich potential actives from large virtual compound libraries through in silico methods rather than real experiments, which can not only accelerate the process of drug discovery but also greatly reduce the time and resource cost. 3-5 Depending on whether the three-dimensional (3D) structure of a target is used or not, VS approaches can be classified into two major categories: ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS). 6 LBVS aims to discover active molecules through the models developed based on a set of known ligands of a target of interest, which may limit its capability to find novel chemotypes. Compared with LBVS, SBVS is considered to be a better choice to discover novel active compounds if the 3D structure of a given target is available. 7 Chao Shen and Junjie Ding are equivalent first authors.

Background and Purpose Thioredoxin‐interacting protein (TXNIP), a regulator of cellular oxidative stress, has been associated with activation of NOD‐like receptor 3 (NLRP3) inflammasome, inflammation and lipid metabolism, suggesting it has a role in the pathogenesis of non‐alcoholic fatty liver disease (NAFLD) in diabetes. In this study we investigated whether TXNIP is involved in type 1 diabetes‐associated NAFLD and whether antioxidants, quercetin and allopurinol, alleviate NAFLD by targeting TXNIP. Experimental Approach Diabetes was induced in male Sprague‐Dawley rats by a single i.p. injection of 55 mg·kg−1 streptozotocin. Quercetin and allopurinol were given p.o. to diabetic rats for 7 weeks. Hepatic function, oxidative stress, inflammation and lipid levels were determined. Rat BRL‐3A and human HepG2 cells were exposed to high glucose (30 mM) in the presence and absence of antioxidants, TXNIP siRNA transfection or caspase‐1 inhibitor, Ac‐YVAD‐CMK. Key Results Quercetin and allopurinol significantly inhibited the TXNIP overexpression, activation of NLRP3 inflammasome, down‐regulation of PPARα and up‐regulation of sterol regulatory element binding protein‐1c (SREBP‐1c), SREBP‐2, fatty acid synthase and liver X receptor α, as well as elevation of ROS and IL‐1β in diabetic rat liver. These effects were confirmed in hepatocytes in vitro and it was further shown that TXNIP down‐regulation contributed to the suppression of NLRP3 inflammasome activation, inflammation and changes in PPARα and SREBPs. Conclusions and Implications Inhibition of hepatic TXNIP by quercetin and allopurinol contributes to the reduction in liver inflammation and lipid accumulation under hyperglycaemic conditions. The targeting of hepatic TXNIP by quercetin and allopurinol may have therapeutic implications for prevention of type 1 diabetes‐associated NAFLD.

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