BASIC INTRODUCTION ABOUT ARTIFICIAL INTELLIGENCE.

1. JOURNAL CLUB PRESENTATION BY:
DIPANKAR NATH.
Assistant Professor.
Department of Pharm.Analysis
HIMALAYAN PHARMACY INSTITUTE.

3. WHY AI??

4. A new type of scientist-AI:
 When the time comes for the history of artificial intelligence (AI) to be written it was 12
June 2007. That was the day that a robot called Adam ended humanity’s monopoly on the
discovery of scientific knowledge — by identifying the function of a yeast gene.
 By searching public databases, Adam generated hypotheses about which genes code for key
enzymes that catalyse reactions in the yeast Saccharomyces cerevisiae, and used robotics to
physically test its predictions in a lab.
 Researchers at the UK universities of Aberystwyth and Cambridge then independently
tested Adam’s hypotheses about the functions of 19 genes; 9 were new and accurate, and
only 1 was wrong.
 In January 2008, the same team announced that Adam’s more advanced robot colleague,
Eve, had discovered that triclosan,a common ingredient in toothpaste, could potentially treat

5. drug-resistant malaria parasites. This identified triclosan as affecting malaria-parasite growth
by inhibiting the DHFR enzyme also the target of the antimalarial drug pyrimethamine.
However,resistance to pyrimethamine is common. The researchers showed that triclosan could
act on DHFR even in pyrimethamine resistant parasites.

8. Artificial intelligence (AI) is the simulation of the human intelligence process by computers.
The process includes acquiring information, developing rules for using the information,
drawing approximate or definite conclusions and self-correction.
 The advancement of AI can be seen as a double-edged sword: many fear that it will
threaten their employment; by contrast, every advance in AI is celebrated because of the
belief that it will vastly contribute to the betterment of society.
The sprouting idea of adopting AI in the drug development process has shifted from hype to
hope.
AI, machine learning and deep learning :
 AI is described as the use of techniques that enable computers to mimic human behaviour .
 AI also contains a subfield called machine learning (ML), which uses statistical methods

9. with the ability to learn with or without being explicitly programmed .
 ML is categorized into supervised, unsupervised and reinforcement learning .
 Supervised learning comprises classification and regression methods where the predictive
model is developed based upon the data from input and output sources. Output from
supervised ML entails disease diagnosis under the subgroup classification; and drug
efficacy and ADMET prediction under the subgroup regression.
 Unsupervised learning comprises clustering and feature-finding methods by grouping and
interpreting data based solely on input data. Through unsupervised ML, outputs such as
disease subtype discovery from clustering and disease target discovery from feature-
finding methods can be attained.
 Reinforcement learning is largely driven by decision making in a given environment and
its execution to maximize its performance .

10. The outputs from this type of ML include de novo drug design under decision making and
experimental designs under execution – where both can be achieved via modelling and
quantum chemistry .
A further subfield of ML called deep learning (DL) uses artificial neural networks that
adapt and learn from the vast amount of experimental data . Big data and associated data
mining and algorithm methods could provide us with the capacity to discover new
compounds that could potentially be new drugs, uncover or repurpose drugs that could be
more potent when used individually or in combination and improve the area of
personalized medicine based on genetic markers. The striking difference that makes DL a
subfield of AI is the flexibility in the architecture of neural networks such as
convolutional neural network (CNNs), recurrent neural networks (RNNs) and fully
connected feed-forward networks. It is believed, with proper establishment of methods in

11. AI, we will witness the transition into an era of minimized failures in clinical trials and a
faster, cheaper and effective drug development processes.

15. Machine learning applications in the drug discovery and their required data characteristics

17. Applications of AI in drug development:
 The tasks of finding successful new drugs is daunting and predominantly the most difficult
part of drug development. This is caused by the vast size of what is known as chemical
space(Cheminformatics), which is estimated to be in the order of 1060molecules.
 AI have become versatile tools that can be applied in various stages of drug development,
such as identification and validation of drug targets, designing of new drugs, drug
repurposing, improving the R&D efficiency, aggregating and analyzing biomedicine
information and refining the decision making process to recruit patients for clinical trials.
 The other uses of AI in drug development include the prediction of feasible synthetic routes
for drug-like molecules, pharmacological properties, protein characteristics as well as
efficacy, drug combination and drug–target association and drug repurposing.

18.  Identification of new pathways and targets using omics analysis becomes possible via the
generation of novel biomarkers and therapeutic targets, personalized medicine based on
omics markers and discovering the connections between drugs and diseases .
 DL has demonstrated outstanding success in proposing potent drug candidates and accurately
predicting their properties and the possible toxicity risks .
AI in understanding the pathway or finding molecular targets:
 AI has transformed the methods of pathway or target identification to treat diseases. This was
possible owing to the incorporation of genomics information, biochemical attributes and
target tractability .
 A computational prediction application known as ‘Open Targets’ – a platform consisting of
gene–disease association data – and it was reported that animal models exhibiting a disease

19. relevant phenotype with a neural network classifier of >71% accuracy provided the most
predictive power.
 IBM Watson for Drug Discovery, an AI platform, has identified five new RNA-binding
proteins (RBPs) linked to pathogenesis of a neurodegenerative disease known as
amyotrophic lateral sclerosis (ALS).
AI in finding the hit or lead:
 AI in the discovery of small drug-like molecules concerns the utilization of chemical
space. Chemical space provides the stage for identifying novel and high-quality molecules
because it is possible to computationally enumerate the probable organic molecules .
 ML techniques and predictive model software also contribute to the identification of target-
specific virtual molecules and association of the molecules with their respective target
while optimizing the safety and efficacy attributes .

20.  AI systems can reduce the attrition rates and the R&D expenditure by decreasing the
number of synthesized compounds that are subsequently tested in either in vitro or in vivo
systems. A variety of in silico techniques for profile selection such as virtual ligand or
structure-based design approaches can be used .
 DL becomes useful in instances where structural data are insufficient. Thus, phenotypic
data or disease, biology or molecule network-based algorithms can be used.
 AI techniques can be used to increase the success rates in drug development, whereas the
AI techniques that are in development must be validated before applying to the drug
development process.
 AI is valuable owing to its ability to prioritise molecules based on the ease of synthesis or
develop tools that are effective for the optimal synthetic route.

21. AI in synthesis of drug-like compounds:
 Drug-like molecules are compounds that obey Lipinski’s rule of five: (i) molecular weight
<500 Da; (ii) H-bond donors <5; (iii) H-bond acceptor <10; and (iv) calculated Log P
(cLogP) <5
 For the synthesis of drug-like molecules, retrosynthesis is used extensively by chemists.
 The first step in the retrosynthetic approach is to analyse the target compounds recursively
and to sequentially convert them into smaller fragments or building blocks that can be easily
purchased or prepared.
 The second step is to identify the reactions that will convert these fragments into target
compounds The second step is the most challenging because it is difficult for the human
brain to interrogate the vast number of relevant organic reactions available in the literature
to pick the most plausible reaction.

22.  AI would aid in predicting the best sought-after reactions by filling the voids that cause high
failure in expected organic synthesis (commonly known as ‘out of scope’ compounds).
 The voids in organic synthesis are mainly the result of unpredictable steric and electronic
effects and incomplete understanding of the reaction mechanism. Currently, several
computer aided organic compound synthesis (CAOCS) systems are available to assist
chemists in selecting the synthesis route.
 Seglar et al. have developed a new AI platform named 3N-MCTS, which combines three
different deep neural networks with Monte Carlo Tree Search (MCTS) for CAOCS. This
platform can filter out the most promising building blocks and select only well-known
reactions for the synthesis of target compounds. The platform, 3N-MCTS, was proven to be
much faster and better than that of traditional computer-assisted retrosynthesis systems.

23.  The platform was able to propose feasible synthesis routes without unreasonable steps in a
relatively short time. However, quantitative prediction of enantiomeric or diastereomeric
ratios and devising natural product syntheses plans are an unmet need.
Predicting the mode-of-action of compounds using AI:
 AI platform that can predict the on- and off-target effects and in vivo safety profile of
compounds before they are synthesized excites those involved in the drug development
process – particularly medicinal chemists.
 The availability of such platforms reduces the drug development time, R&D costs and
attrition rates.
 A few examples of such platforms are DeepTox (predicts toxicity of new compounds) and
PrOCTOR (predicts the probability of toxicity in clinical trials). The predictive accuracy of
these platforms could be improved if a bigger and refined dataset on toxicity and

24.  therapeutic profile of a varied set of compounds is made available. However, this can only
be achieved if there is a willingness to share data among the industry .
 Recently, an innovative AI tool, SPiDER, was developed as an alternative to
chemoproteomics to advance natural products for drug discovery .
 SPiDER was used to predict the molecular target of b-lapachone, a clinical-stage natural
naphthoquinone with antitumour activity. The platform predicted b-lapachone as an
allosteric and reversible modulator of 5- lipoxygenase (5-LO). The prediction is validated
using a 5-LO functional assay .
 Another AI tool: read-across structure–activity relationships (RASAR) which links
molecular structures and toxic properties by mining a large database of chemicals, was
reported to accurately predict the toxicity of unknown compounds.

25. AI in selection of a population for clinical trials:
 An ideal AI tool to assist in clinical trials should recognise the disease in patients, identify
the gene targets and predict the effect of the molecule designed as well as the on- and off-
target effects .
 A novel AI platform called AiCure was also developed as a mobile application to measure
medication adherence in a Phase II trial of subjects suffering from schizophrenia, where it
was reported that AiCure increased adherence 25% compared with the traditional ‘modified
directly observed therapy .
 Patient selection for a clinical trial is a crucial process. The development of AI approaches
to identify and predict human-relevant biomarkers of disease allows the recruitment of a
specific patient population in Phase II and III clinical trials. The AI predictive modelling in
selection of a patient population would increase the success rate in clinical trial.

26. AI in drug repurposing:
 With AI, the drug repurposing process becomes more attractive and pragmatic
 In a study performed by Aliper et al., it was demonstrated that DNNs could classify
complex drug action mechanisms on the pathway level, thus classifying drugs into
therapeutic categories according to their functional class, efficacy, therapeutic use and
toxicity .
 Additionally, the advances in precision medicine have resulted in the creation of next-
generation AI that offers the ability to design drug molecules from the generative
adversarial networks (GANs). GANs are an astounding technology that uses DL to produce
photo-realistic pictures from text descriptions.
 Another next-generation AI method used in in silico medicine is reinforcement learning.
The advantage of this AI technique is that it is less dependent on learning from datasets,

27. thus the networks can recognize certain strategies in drug molecule design.
 Zhavoronknov’s team designed algorithms that can reconstruct missing features from half-
full datasets and interpret the differences in normal and diseased profiles within complex
data. One aspect of interest is the possibility of AI in designing drugs with fewer side
effects. The AI algorithm is also being trained to differentiate between cardiotoxic and non-
cardiotoxic drugs using gene expression data from cells incubated with different drugs. The
team is currently competing their AI-designed molecules against those designed by
chemists .
 An attempt was made to capture innate knowledge that assists experienced medicinal
chemists in identifying promising drug candidates by looking using mobile
electroencephalography to measure responses to the structure and numerical properties of a
molecule.

28. AI in polypharmacology:
 Currently, the ‘one-disease–multiple-targets’ paradigm dominates over the ‘one-disease–
one-target’ paradigm because of deeper understanding of pathological processes in diseases
at the molecular level. One-disease–multiple-targets is termed polypharmacology.
 Many databases, such as ZINC, PubChem, Ligand Expo, KEGG, ChEMBL, DrugBank,
STITCH, BindingDB, Supertarget, PDB, among others, are available to integrate diverse
information of molecular pathways, crystal structures, binding affinities, drug targets,
disease relevance, chemical properties and biological activities.
 A success story of an AI application in designing polypharmacological agents was recent-
ly published in the literature – the authors developed a computational platform, DeepDDI,
for better understanding of drug– drug interactions and associated mechanisms and

29. prediction of alternative drugs for intended clinical use without negative health effects.
Partnerships between artificial intelligence (AI) and pharmaceutical companies and
areas of collaboration in drug development.
 Leading biopharmaceutical companies believe a solution is at hand. Pfizer is using IBM
Watson, a system that uses machine learning, to power its search for immuno-oncology
drugs.
 Sanofi has signed a deal to use UK start-up Exscientia’s artificial-intelligence (AI) platform
to hunt for metabolic-disease therapies.
 Roche subsidiary Genentech is using an AI system from GNS Healthcare in Cambridge,
Massachusetts, to help drive the multinational company’s search for cancer treatments.
 Atomwise Introduced the first structure-based deep CNN, called AtomNet, designed to
predict the bioactivity of small molecules for drug discovery applications. It shows how AI

30. exploits feature locality and hierarchical composition to the modeling of bioactivity and
chemical.
 TwoXAR uses AI to screen compound libraries for efficacy against a disease to discover
new drug candidates from a public library, and identify biologic targets.
 ReviveMed Discover detailed molecular pathways associated with a specific disease.
 Phenomic AI Using computer vision and reinforcement learning to enable discovery of the
next generation of therapies against cancer.

36. Conclusion:
 Currently, there are no developed drugs that have utilised AI approaches but, based on the
advances, it is likely that it will take a further 2–3 years for a drug to be developed.
Interestingly, experts strongly believe that AI will permanently change the pharmaceutical
industry and the way drugs are discovered .
 This creates the suitable workspace whereby AI and medicinal chemists can work closely
together.
 AI will be able to help in analyze huge datasets where as medicinal chemists can train
machines, set algorithms or optimize the analyzed data for a speedier and accurate drug
development process

38. Some of the institutes in India which offer Master courses in Artificial
Intelligence are:
 University of Hyderabad.
 IIT Bombay.
 IIT Madras.
 IISc Bangalore.
 ISI Kolkata
 NIT Warangal(Diploma)
 IIT Delhi (Diploma)

39. REFERENCES:
 Mak, Kit-Kay. et al.(2018) Artificial intelligence in drug development: present status and
future prospects. Drug Discovery Today, 1-8.
 Vamathevan ,Jessica. et al.(2019) Applications of machine learning in drug discovery and
development. Nature Reviews Drug Discovery, 1. 10.1038/s41573-019-0024-5.
 Fleming,Nic (2018) Spotlight on Biopharmaceuticals careers, Computer Calculated
Compounds. Nature,(557)s55-57.