Abstracts: December 12, 2023

QIN: Yeah, uh, we work on the problem of molecular docking, a computational modeling method used to predict the preferred orientation of one molecule when it binds to a second molecule to form a stable complex. So it aims to predict the binding pose of a ligand in the active site of a receptor and estimate the ligand-receptor binding affinity. This problem is very important for drug discovery and development. Accurately predicting binding poses can provide insights into how a drug candidate might bind to its biological target and whether it is likely to have the desired therapeutic effect. To make an analogy, just like a locker and a key, protein target is a locker, while the ligand is a key. We should carefully design the structure of the key so that it can perfectly fit into the locker. Similarly, the molecular structure should be accurately constructed so that the protein can be well bonded. Then the protein function would be activated or inhibited. Molecular docking is used intensively in the early stages of drug design and discovery to screen a large library of hundreds of thousands of compounds to identify promising lead compounds. It helps eliminate poor candidates and focus on experimental results of those most likely to bind to the target protein well. So clearly, improving the accuracy and also the speed of docking methods, like what we have done in this work, could accelerate the development of new life-saving drugs. 

HUIZINGA: So, Lijun, tell us how your approach builds on and/or differs from what’s been done previously in this field. 

WU: Sure, thanks, yeah. So conventional protein-ligand docking methods, they usually take the sampling and scoring ways. So … which … that means, they will use first some sampling methods to generate multiple protein-ligand docking poses as candidates. And then we will use some scoring functions to evaluate these candidates and select from them and to choose the best ones. So such as DiffDock, a very recent work developed by MIT, which is a very strong model to use the diffusion algorithm to do the sampling in this kind of way. And this kind of method, I say the sampling and scoring methods, they are accurate with good predictions, but of course, they are very slow. So this is a very big limitation because the sampling process usually takes a lot of time. So some other methods such as EquiBind or TANKBind, they treat the docking prediction as a regression task, which is to use deep networks to directly predict the coordinates of the atoms in the molecule. Obviously, this kind of method is much faster than the sampling methods, but the prediction accuracy is usually worse. So therefore, our FABind, which … aims to provide a both fast and accurate method for the docking problem. FABind keeps its fast prediction by modeling in a regression way, and also, we utilize some novel designs to improve its prediction accuracy. 

HUIZINGA: So, Lijun, let’s stay with you for a minute. Regarding your research strategy on this, uh, how would you describe your methodology, and how did you go about conducting this research? 

WU: OK, sure. So when we’re talking about the detailed method, we actually build an end-to-end deep learning framework, FABind, here. So for the protein-ligand docking, FABind divides the docking task as a pocket prediction process and also a pose prediction process. But importantly, we unify these two processes within a single deep learning model, which is a very novel equivalent graph neural network. Here, the pocket means a local part of the whole protein, which are some specific amino acids that can bind to the molecule in the structure space. So simply speaking, this novel graph neural network is stacked by some identity graph neural networks. And the graph neural layer is carefully designed by us, and we use the first graph layer for the pocket prediction and the later layers to do the pose prediction. And for each layer, there are some message passing operations we designed. The first one is an independent message passing, which is to update the information within the protein molecule itself. And the second one is the cross-attention messenger passing, which is to update the information between the whole protein and also the whole molecule so we can then let each other have a global view. And the last one is an interfacial messenger passing, which is to do the update, and we can message pass the information between the closed nodes between the protein and the molecule. So besides, there are also some small points that will help to get an accurate docking model. For example, we use a scheduled training technique to bridge the gap between the training and the inference stages. And also, we combine direct coordinate prediction and also the distance map refinement as our optimization method. 

HUIZINGA: Well, listen, I want to stay with you even more because you’re talking about the technical specifications of your research methodology. Let’s talk about results. What were your major findings on the performance of FABind?

WU: Yeah, the results are very promising. So first we need to care about the docking performance, which is the accuracy of the, uh, docking pose prediction. We compare our FABind to different baselines such as EquiBind, TANKBind, and also, I talked before about the recent strong model DiffDock, developed by MIT. So the results showed that our docking prediction accuracy are very good. They achieve a very competitive performance to the DiffDock like that. But specifically, we need to talk about that the speed is very important. When compared to DiffDock, we achieved about 170 times faster speed than DiffDock. So this is very promising. Besides, the interesting thing is that we found our FABind can achieve very, very strong performance on the unseen protein targets, which means that the protein structure that we have never seen before during the training, we can achieve very good performance. So our FABind achieves significantly better performance with about 10 percent to 40 percent accuracy improvement than DiffDock. This performance demonstrates that the practical effectiveness of our work is very promising since such kinds of new proteins are the most important ones that we need to care for a new disease. 

HUIZINGA: Tao, this is all fascinating, but talk about real-world significance for this work. Who does it help most and how? 

QIN: Yeah. As Lijun has introduced, FABind significantly outperforms earlier methods in terms of speed while maintaining competitive accuracy. This fast prediction capability is extremely important in real-world applications, where high-throughput virtual screening for compound selection is often required for drug discovery. So an efficient virtual screening process can significantly accelerate the drug discovery process. Furthermore, our method demonstrates great performance on unseen or new proteins, which indicates that our FABind possesses a strong generalization ability. This is very important. Consider the case of SARS-CoV-2, for example, where our knowledge of the protein target is very limited at the beginning of the pandemic. So if we have a robust docking model that can generalize to new proteins, we could conduct a large-scale virtual screening and, uh, confidently select potentially effective ligands. This would greatly speed up the development of new treatments. 

HUIZINGA: So downstream from the drug discovery science, benefits would accrue to people who have diseases and need treatment for those things. 

QIN: Yes, exactly. 

HUIZINGA: OK, well, Tao, let’s get an elevator pitch in here, sort of one takeaway, a golden nugget, uh, that you’d like our listeners to take away from this work. If, if there was one thing you wanted them to take away from the work, what would it be? 

QIN: Yeah, uh, thanks for a great question. So I think one sentence for takeaway is that if for some researchers, they are utilizing molecular docking and they are seeking an AI-based approach, our FABind method definitely should be in their consideration list, especially considering the exceptional predictive accuracy and the high computational efficiency of our method.

HUIZINGA: Finally, Tao, what are the big questions and problems that remain in this area, and what’s next on your research agenda? 

QIN: Actually, there are multiple unaddressed questions along this direction, so I think those are all opportunities for further exploration. So here I just give three examples. First, our method currently tackles rigid docking, where the target protein structure is assumed to be fixed, leaving only the ligand structure to be predicted. However, in a more realistic scenario, the protein is dynamic during molecular binding. So therefore, exploring flexible docking becomes an essential aspect. Second, our approach assumes that the target protein has only one binding pocket. In reality, a target protein may have multiple binding pockets. So this situation will be more challenging. So how to address such kind of significant challenge is worth exploration. Third, in the field of drug design, sometimes we need to find a target or we need to find a drug compound that can bind with multiple target proteins. In this work, we only consider a single target protein. So the accurate prediction of docking for multiple target proteins poses a great challenge. 

HUIZINGA: Well, Tao Qin and Lijun Wu, thank you for joining us today. And to our listeners, thanks for tuning in.  

[MUSIC PLAYS] 

If you’re interested in learning more about this work, you can find a link to the paper at aka.ms/abstracts or you can find it on arXiv. See you next time on Abstracts! 

[MUSIC FADES]