Useful introductory books and blogposts on neural networks

Here's a list of books and blogposts on neural networks and related aspects that I have found particularly useful. In general, I like very simple examples - preferably with python code - to introduce me to a topic.

Books

Make Your Own Neural Network

This book is an excellent place to start. The book explains the basics of NNs and guides you through writing your own 3-layer NN code from scratch and applying it to the MNIST set. The book even introduces you to Python, so this is something virtually anyone can do. My only (minor) complaint is that the code uses classes, which can be quite difficult for beginners to grasp and it not really needed here.

Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More
This book offers brief and to-the-point descriptions of some of the major classes of NNs, such CNN and RNN in the first chapters and then walks you though many interesting applications using the DeepChem library. This book gets you started using NNs very quickly and is an excellent supplement to the more basic or more theoretical approaches in this list.

Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning
This is a more formal treatment of deep learning but I still found it (mostly) very readable and there are several useful pseudo-code examples with Python equivalents. The topics are discussed in roughly chronological order, so you also get a good feel for how the NN field developed including major milestones.

Blogposts

Build a Recurrent Neural Network from Scratch in Python – An Essential Read for Data Scientists

This is basically the equivalent of Make Your Own NN but for a RNN applied to a toy problem.

Introduction to Convolutions using Python and Playing with convolutions in Python

Both posts offer some very simple Python examples of what convolution actually means for images.

How to do Deep Learning on Graphs with Graph Convolutional Networks

A very simple Python introduction to graph convolution, which works quite bit differently from image convolution.

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Comparison of SMILES-, DeepSMILES-, SELFIES-, and graph-based genetic algorithms Part 2

This post is a follow up to this post. There are two changes:

In that post I generated the data for the string based methods using my graph-based GA (GB-GA) code interfaced with new, string-based, crossover and mutation code. However, this involves going back and forth between graph and string-based representations which could potentially change the atom order. To make sure that doesn't happen I have now written a stand alone string-based GA code, where strings only are converted to graphs when computing the score and graphs are never converted back to strings.

I also had a another look at Brown et al.'s GA code and noticed that they remove duplicates from the population for each generation, which my code didn't. So implemented that as well for both the graph- and string-based methods. In the table below I list the best results, where the original implementation that does not remove duplicates are indicated by a "*".

For GB the removal of duplicates only improves results for celecoxib, where it is now rediscovered 8 times instead of 4. Tiotixene is not rediscovered and troglitazone is only found once with GB-GA, when duplicates are removed.

The new string-based implementation improves results for SMILES and DeepSMILES, with the exception of SMILES for troglitazone, which is discovered once using the old implementation. For SELFIES the new implementation is a little bit worse, but I would say the difference is within the statistical uncertainty. 

GB still tends to outperform string based methods, though they all perform much better than I had expected. Amazingly, DeepSMILES and SELFIES do not appear to offer a clear advantage over SMILES with the exception of troglitazone, where DeepSMILES performs significantly better.

Here are the high scoring molecules found with string based methods. Some of the molecules have radical centers (red boxes) due to misplaced chiral centers.

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Comparison of SMILES-, DeepSMILES-, SELFIES-, and graph-based genetic algorithms

This post is a follow up to this post. There are three main changes: 1) I have included Emilie's code in my code, 2) I have extended the implementation to SELFIES, and 3) the initial pool of molecules is now constructed exactly as described by Brown et al. (i.e. we use the 100 highest scoring molecules from ChEMBL, but remove molecules with scores higher than 0.323).

As before, I run the 10 GA searches, each for 1000 generations, and record the overall highest score found and the average high score. If the score is 1.00 I also record the number of times I found it, in parentheses. I also record the CPU time on 8 cores (note that I stop the search once the score is 1.00, so the time is not necessarily for 10 x 1000 generations).

Here are the high scoring molecules found with string based methods

Bottom line, DeepSMILES and SELFIES perform about the same, and both tend to outperform SMILES for rediscovery using GA.


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Comparison of SMILES-, DeepSMILES- and graph-based genetic algorithms

Emilie is wrapping up her bachelor project and writing the report and here are some preliminary results (which are likely to change a bit).

I recently developed a graph-based genetic algorithm that seems to work pretty well. The crossover and mutation code is about 250 lines with a lot of hyperparameters that mainly specify the probabilities of performing different crossovers and mutations.

The question is whether all this was really necessary or could I have gotten away with about 25 lines of code that perform crossover and mutation operations on SMILES strings? For crossover you simply cut two strings at random places and recombine the fragments, e.g. OCC|C and CC|N (where "|" indicates the cut) yield OCCN and CCC and for mutation you simply change one character to another, e.g. CCC becomes C=C.

The potential problem with using SMILES is that one can imagine many scenarios where this wouldn't work, e.g. OC(|C)C and C1C|O1 would yield OC(O1 and C1CC)C, which are not valid SMILES string.  But can you still find molecules with the desired properties using this approach? If so, do the molecules look very different than the ones you find with the graph-based approach? Which approach is more efficient?

And what about the DeepSMILES representation developed by O'Boyle and Dalke? Here, OC(|C)C and C1C|O1 are written as OC|C)C and CC|O3, which would yield OCO3 and CCC)C - both valid DeepSMILES strings.

Finding molecules with specific penalised logP values
We start by looking at what Brown et al. call a "trivial optimisation objective": finding molecules with a particular modified logP values. We use the same Gaussian modifier approach with a standard deviation of 2 logP units and select the initial population from the first 1000 molecules in the ZINC data set (after removing molecules with logP values within 2 units of the target). The mating pool size and mutation rates are 20 and 10%, respectively. The table shows the average number of generations (based on 10 runs) needed to find a molecule with a logP values within 0.01 of the target.

It is clear that a SMILES-based GA has no problems meeting the objective, but that using DeepSMILES is more effective both in terms of number of required generations and CPU time. The latter, because the percentage of valid strings generated by crossover and mutation (the succes rate) is considerably higher for DeepSMILES as expected. For this target there appears to be no real advantage in using graph-based GA.

Rediscovering a molecule
The next target is considerably harder: generating molecules with a Tanimoto similarity of 1.0 with a target molecule (naphthalene, celecoxib, or tiotixene).  A Tanimoto similarity of 1.0 means that each atom has the same bonding pattern out to a certain radius (here 4 bonds), i.e. that the molecules are very, very similar. The population size is 100 and the mating pool size is 200. The initial populations is 100 molecules with the highest Tanimoto scores to the target (but with Tanimoto scores less than 0.323, following Brown et al.) found among the first 50,000 molecules in the ZINC data set.

Here it's clear that the graph-based approach offer an advantage over string-based methods, while DeepSMILES only offers an advantage over SMILES in terms of effciency. The Tanimoto score goes from 0 to 1, so the molecules found for the tiotexene search using graph-based GA look significantly more like tiotexene than those found with the string based methods. (When I run celecoxib search I succeed 5/10 times, and we are still trying to find the cause of this difference.)

Finding molecules that absorb at a particular wavelength
Inspired by the study of Tsuda and co-workers, we search for molecules that absorb at 400 and 800 nm. We use Grimme's semiempirical sTDA-xTB method to estimate the absorption wavelength and oscillator strength based on a low energy MMFF-optimised structure. We use a Gaussian(400/800,50) scoring function for the wavelength and a LinearThresholded(0.3) scoring function for the oscillator strength (0.3 is the oscillator strength computed for indigo dye). The population and mating pool sizes are 20 and 40, respectively and the mutation rate is 15%. We select the initial population from the first 1000 molecules in the ZINC data set (after removing molecules with absorption wavelengths within 300 nm of the target). The GA search is stopped if the top-scoring molecule has an absorption wave length within 5 nm of the target value.

It turns out that the find molecules that absorb relatively strongly at 400 and 800 nm is a relatively easy optimisation problem.

Conclusion
If there are very few ways to meet the target then graph-based GA performs better than string-based GA methods, but otherwise not. DeepSMILES-based GA is computationally more efficient than SMILES-based GA in many cases.  It would be interesting to test the newly introduced SELFIES representation.



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