Advancing drug discovery via GPU-based deep learning

Figures & data

Table 1. A comparison of technical aspects of two recent GPU and CPU implementations.

Aspect	GPU	CPU
Calculation	Streaming multiprocessors featuring hundreds to thousands of arithmetic logic units (ALUs).	Quad core processor with each core containing a few ALUs.
Memory	Small cache but large registry space. High memory bandwidth for easy access of slow graphics card DRAM.	Many transistors dedicated to caching. High probability that data needed is still present on the chip when requested.
Control	Few control structures, mostly dedicated to organizing and distributing calculations into a parallelism hierarchy to execute tasks.	Large control area including speculative loading of instructions. High speculative fetch hit rates yield execution speedup.
Hardware concept	High number of simple computing cores. Registry caching of core calculations enables re-use of results for speed and scalability. Excellent for tasks with small in-memory footprint.	Few cores, but large caches together with lots of control units allow for high clock rates and quick switching between many different tasks (high branching capability).

Figure 1. Comparison of ANN compute times using CPU and CPU+GPU hardware. Hardware used: (Desktop) Dual 2.66 GHz 6-core Intel Xeon X5650 processors; (Server) Four 2.3 GHz 16-core AMD Opteron 6376 processors; (GPU1) Dual 2.4 GHz Intel Xeon E5-2620 processors and an ASUS GeForce GTX1080 STRIX graphics card; (GPU2) Four 3.6 GHz Intel Core i7 6850K processors and an ASUS GeForce GTX1080Ti STRIX graphics card.

Table 2. Directions and possibilities in DNN-based drug discovery.

Direction	Pros	Cons
Replace ‘shallow’ cheminformatics model with a DNN	Most direct approach to prospective experimental studies.	No guarantee of improved modeling ability using existing descriptors.
Automatically generate molecular representation and network topology	High-risk, high-reward tactic. Leverages learning the concept of a molecule from domain data (e.g. atomic coordinates). No specific descriptor engineering required.	High data volumes required. Substantial expertise required in both deep-learning and cheminformatics. Long development time. Potentially no obvious human interpretability.
Simultaneous input of heterogeneous data	Utilizes a DNN’s transfer learning capabilities. Reminiscent of ensemble methods which have proven applicability.	All of the above, and potentially high risk of overfitting with failure in prospective validation.