That library is nature—and its language is DNA.

The Earth BioGenome Project (EBP) is a global scientific initiative working to read that language by creating high-quality genome sequences for Earth's biodiversity. Its vision is to build a genomic foundation for understanding animals, plants, fungi and other eukaryotic life.

Why does this matter?

Because every species carries information shaped by millions of years of evolution. A wild plant may hold genetic clues for surviving drought. A marine organism could reveal new biological processes. A threatened species may carry genetic diversity that could help it adapt to a changing environment.

By sequencing genomes, scientists can move beyond simply knowing what species exist. They can begin to understand how they evolved, how they adapt and what makes them resilient.

The possibilities reach far beyond biology. This knowledge could support biodiversity conservation, climate resilience, sustainable agriculture, medicine and new discoveries in biotechnology.

But there is also a race against time. Habitats are changing, populations are declining, and species can disappear before we fully understand them.

That makes EBP more than a technology project. It is an attempt to create a lasting genetic record of life on Earth—while that life is still here.

For centuries, humans have explored, named and documented the natural world.

Now, we have the opportunity to read it.

Every genome is a story.

The Earth BioGenome Project is helping us write the world's most extraordinary library—the book of life itself.

Read and explore more about it @ https://www.earthbiogenome.org/

There are Neanderthal bones. Denisovan remains. Fossils from ancient relatives of our species. Each specimen gives us another piece of the story of how Homo sapiens came to be.

But now imagine that one of the most important characters in that story has left behind no fossil, no skull, no bone, and no genome.

Nothing that a paleontologist could put behind glass.

And yet, somehow, that ancient population is still speaking.

It is speaking through us.

A fascinating new study published in Science (https://www.science.org/doi/10.1126/science.aef8874?) introduces a computational method called TRACE, which allows scientists to search modern human genomes for genetic traces of ancient populations that have never been directly identified.

For years, scientists have known that our ancestors interacted with Neanderthals and Denisovans. Their DNA still survives in modern human populations. But these may not have been the only ancient humans who crossed paths with our ancestors.

Using TRACE, researchers found evidence of an unknown archaic population that contributed DNA to the ancestors of modern humans before humans spread beyond Africa.

Even more surprisingly, they found evidence of a much older lineage—possibly around 1.8 million years old—whose genetic legacy may have reached some modern humans indirectly through Denisovans.

The extraordinary part is that researchers did not need DNA from these mysterious populations to detect them. Instead, they studied the ancestry of modern genomes and looked for genetic patterns that revealed unusually ancient branches of our family history.

It is almost like finding the footprints of someone who disappeared thousands of years ago—without ever finding the person themselves.

These discoveries suggest that human evolution was far more complicated than a simple family tree. Ancient human populations repeatedly separated, migrated, met and exchanged genes. Some eventually disappeared, but pieces of their genetic legacy survived.

And that means our DNA is more than a biological instruction manual.

It is also an archive of human history.

Some of the people recorded in that archive may never have a name or a fossil. But thanks to methods like TRACE, their genetic echoes are finally becoming visible.

The next big discovery about our ancient past may not come from a fossil in the ground.

It may already be inside us.

Reference: Zhang et al., “Recovering signatures of archaic hominin introgression using ancestral recombination graphs,” Science (2026), DOI: 10.1126/science.aef8874. Detail paper @ https://www.science.org/doi/10.1126/science.aef8874?

The TRACE repository (https://github.com/YulinZhang9806/trace) contains the Python package and command-line tools used to infer archaic admixture tracts from ancestral recombination graphs.

However, pangenome graphs can be highly complex, making them difficult to visualize and interpret. A recent study published in BMC Bioinformatics introduces PG2 (PanGenoGrapher), an open-source, web-based tool designed to address this challenge. The source code and user guide are openly available on GitHub at https://github.com/iVis-at-Bilkent/pangenographer. A publicly accessible sample deployment is hosted at http://pg2.cs.bilkent.edu.tr. In addition, a demonstration video illustrating the primary use cases of PG2 is available at https://www.youtube.com/watch?v=yCd7-aGY6CQ.

PG2 combines advanced graph-layout algorithms with an interactive visualization platform. It allows researchers to explore genomic paths, identify variations, and examine relationships between different parts of a pangenome graph more easily.

This is important because visualization can play a major role in bioinformatics. When complex genomic information is presented clearly, researchers can more easily identify patterns, understand genetic variation, and generate new biological insights.

The development of PG2 represents a step toward making pangenome analysis more accessible and intuitive. As genomic datasets continue to grow and graph-based representations become more common, tools like PG2 can help researchers navigate this increasing complexity.

Ultimately, PG2 demonstrates how combining genomics, graph algorithms, and interactive visualization can make sophisticated biological data easier to understand and analyze.

Reference: Solun, G. K., Dogrusoz, U., Bingöl, Z., & Alkan, C. (2026). PG2: algorithms and a web-based tool for effective layout and visual analysis of pangenome graphs. BMC Bioinformatics. DOI: 10.1186/s12859-026-06555-4.

A new study in Science offers a different and surprisingly sweet perspective. Researchers Jennie Brand-Miller, Karen Hardy, David Raubenheimer, and Les Copeland suggest that sugars from fruits and honey, and later starch from cooked plants, may have been key in helping the human brain evolve.

The brain’s carbohydrate problem

The human brain uses a lot of energy. Even though it is only about 2% of an adult’s body weight, it takes up a large share of the body’s resting energy, mainly using glucose as fuel. Over millions of years, as our ancestors’ brains grew from about 300 grams in Australopithecus afarensis to around 1,500 grams in modern humans, the need for easy-to-access glucose would have gone up a lot.

The researchers, therefore, asked a simple question: where did all that glucose come from?

Their analysis suggests that early hominins may have gotten much of their glucose from naturally sweet foods. Fruit and honey could give them easy access to sugars before they learned to digest cooked starch well. The study’s models show that sugars may have made up a big part of their diet.

From fruit and honey to cooked starch

The story did not end with fruit.

The researchers suggest that humans shifted from eating sweet foods to eating cooked starchy foods. Once people learned to control fire and process food, cooking made starchy plants much easier to digest and turned them into a key source of glucose.

From this perspective, new ways of preparing food, like pounding, processing, and cooking, were not just about making meals softer or tastier. These changes may have given our ancestors access to much more carbohydrate energy.

This gives us a more detailed view of how the human diet evolved. Animal foods were clearly important, but carbohydrates may have been just as important for meeting the high energy needs of a growing brain.

A different way to think about the human diet

The study does not claim that our ancestors ate sugar the way we do today. There is a big difference between eating whole fruits or natural honey and eating the highly processed, refined sugars common now.

Instead, the research points to something bigger. Human evolution may have depended on our ability to find, process, and get energy from many different foods.

Seasonal fruit, honey, underground plant foods, and later cooked starches could have been important sources of glucose. The researchers think that changes in how available these foods were may have shaped how our ancestors searched for food and even affected human evolution.

What does this mean today?

The findings should not be interpreted as a license to eat more refined sugar. Instead, they challenge the simple idea that carbohydrates were less important than meat in human evolution. Our ancestors survived and evolved by exploiting different nutritional opportunities—and by developing technologies that made previously difficult foods more useful.

So perhaps the history of the human brain was not written by meat alone. ISo maybe the story of the human brain is not just about meat. It could also be about fruit, honey, roots, grains, and our unique ability to turn plants into energy. sweeter than we once imagined.

Read more about it at https://www.science.org/doi/epdf/10.1126/science.aed8437

The bones sat quietly in a fossil collection for years. One was part of a femur; the other, a tibia. They looked like the remains of an unusually large ancient human. But who did they belong to?

The answer came from an unexpected source: ancient proteins.

Using palaeoproteomic analysis, researchers found a molecular signature in both bones that matches the Denisovan lineage. The discovery identifies the two Penghu fossils as Denisovan and, more importantly, gives scientists their first substantial glimpse of the Denisovans' body size.

And the picture is striking.

One individual is estimated to have been about 1.8 metres tall and weighed around 83 kilograms. The other may have reached 1.9 metres and about 91 kilograms. Their leg bones rank among the largest known from Pleistocene Homo.

These were not small or fragile people.

They were powerful, heavily built humans moving through Ice Age eastern Asia.

But the bones tell an even more intriguing story. The femur has a pronounced ridge called a femoral pilaster—a feature particularly associated with modern human hunter-gatherers and increased mechanical strength during terrestrial movement. Why would a Denisovan, with an otherwise strongly archaic skeleton, possess this modern-looking feature?

Perhaps these Denisovans travelled extensively across the landscape. Perhaps their bodies were shaped by a demanding hunting lifestyle. Or perhaps, the researchers suggest, genetic exchange with early modern humans contributed to some of these features.

Their extraordinary size raises another mystery. A common expectation in human evolution is that populations living closer to the tropics tend to be smaller. Yet these Denisovans lived around 23°N latitude and were exceptionally large. The researchers argue that cold climate alone cannot explain their size, pointing instead toward lifestyle and diet—including evidence that at least one Penghu individual relied heavily on meat.

So, piece by piece, the Denisovans are becoming less mysterious.

What was once a shadowy population known mainly from DNA is beginning to take physical form: large, robust, mobile humans who lived across eastern Asia and whose bodies carried a fascinating mixture of ancient and modern traits.

And perhaps the most remarkable part of this story is where it began—not in a spectacular cave discovery, but with two weathered bones lying among thousands of fossils.

The Denisovans may have left no written history. But their bones are beginning to tell one.

*Note: This research is currently a bioRxiv preprint and has not yet undergone peer review.  Detail at https://www.biorxiv.org/content/10.64898/2026.08.07.743438v1.full.pdf

Population genetics: A concise guide. John Gillespie.The Johns Hopkins University Press (1997)•

Population genetics. J. S. Gale. Wiley (1980)•

Evolutionary genetics. John Maynard-Smith. Oxford University Press (1998)•

The growth of biological thought. Ernst Mayr. Harvard University Press (1985)•

Guns, germs and steel. Jared Diamond. W. W. Norton (2007)•

Evolutionary theory: Mathematical and conceptual foundations. Sean Rice. Oxford University Press (2004)

The vital metaphase stage of cell division, when the sister chromatids migrated to the centre and lined up in a row, and pulled apart using attached microtubules in such a way that half the DNA ends up in each daughter cell. However, before the mitotic spindle‐mediated movement gets start and pulled DNA apart, the chromosomes are free to undergo recombination which involves the exchange of genetic material either between multiple chromosomes or between different regions of the same chromosome.

During recombination, the precise breakage of each strand, exchange between the strands, and sealing of the resulting recombined molecules happens. The “chromosomal breakpoints” refers to these places where they break. Mostly, this process occurs with a high degree of accuracy at high frequency in both eukaryotic and prokaryotic cells. But occasionally this “break and sealing/ break and reattach” process goes wrong and the reattachment happens in the wrong place which usually create disaster (with few exceptions).These chromosome disaster or abnormalities involve the gain, loss or rearrangement of visible amounts of genetic material during cell division. These abnormalities are of two type, the first one is numerical abnormalities  where severe disorders are caused by the loss or gain of whole chromosomes, which affect the copy number of hundreds or even thousands of genes. The second are structural abnormalities which can be unbalanced or balanced. The former are similar to numerical abnormalities in that genetic material is either gained or lost. The natural defects in chromosome segregation are linked to cancer and several genetic diseases (http://en.wikipedia.org/wiki/List_of_genetic_disorders). Therefore, the enzymes involved in regulating cell division are still the attractive drug targets for many diseases.

Apart from certain chromosome abnormalities, these “crossing over” of segments of maternal and paternal chromosomes to form hybrid chromosomes have some evolutionary importance and considered as a driver of genetic variation. Moreover, the chromosome breakage in evolution is considered to be non-random in nature(http://www.ploscompbiol.org/article/info%3Adoi%2F10.1371%2Fjournal.pcbi.0020014). In addition the study of breakpoint regions and non-breakpoint (stable) regions of chromosomes indicates both the regions evolved in distinctly different ways ( http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2675965/). These breakage may lead to genetic diseases or participate to chromosomal rearranmgnets and contributed in development of new species.

I will try to explain the genome hotspots/Evolutionary Breakpoint Regions(EBRs)/fragile regions/weak fragments/  in my next blog.

Software for recombination detection:

RAT http://cbr.jic.ac.uk/dicks/software/RAT/

Breakpointer https://github.com/ruping/Breakpointer

DRP http://web.cbio.uct.ac.za/~darren/rdp.html

RB-finder http://www.ncbi.nlm.nih.gov/pubmed/18707535

LDhat2.0 http://ldhat.sourceforge.net/LDhat2.0/instructions.shtml

Reference:

http://www.nature.com/scitable/topicpage/genetic-recombination-514#

Image: Wikipedia , sciencelearn.org.nz

Recommended Articles:

http://www.friendshipcircle.org/blog/2012/05/22/13-chromosomal-disorders-youve-never-heard-of/

http://web.udl.es/usuaris/e4650869/docencia/segoncicle/genclin98/recursos_classe_%28pdf%29/revisionsPDF/chromosyndromes.pdf

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2775595/table/T2/

http://learn.genetics.utah.edu/content/disorders/chromosomal/

http://www.ncert.nic.in/html/learning_basket/biology/cc&cd.pdf

Now a day, geneticists have developed a language of their own. They are pouring lots of money and energy to read the entire genomic text and understand the gods own code ATGC. It is somewhat fascinating, not only for geneticist but also for non-biologist to know that there are several conserved blocks in genome which remain conserved over hundreds of millions of years. There have been several researches on conserved blocks and non-conserved regions to understand the mechanism and importance of all these regions (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2675965/). The finding indicates conservation and rearrangements of certain evolutionary important genes play an important role in evolution/adaptive changes (http://www.nature.com/nature/journal/v491/n7424/abs/nature11622.html https://academic.oup.com/gbe/article/8/8/2442/2198198/Novel-Insights-into-Chromosome-Evolution-in-Birds , http://science.sciencemag.org/content/346/6215/1311).

But the puzzle remains open, how to correctly define the synteny (presence of two or more genes on the same chromosome) and conserved synteny (presence of two or more genes on chromosome of each of the two species) on several genomes.

Figure: Image generated with Evolution Highway (EH) tool http://eh-demo.ncsa.illinois.edu/ 

Keeping the new approach to define conserved synteny in mind there have been various algorithms developed to identify the conserved homologous synteny blocks (HSB) amongst species. Some of them which were commonly used for synteny detections are:

SyntenyTracker ( http://www-app.igb.uiuc.edu/labs/lewin/donthu/Synteny_assign/html/),

SyntenyTracker was shown to be an efficient and accurate automated tool for defining HSBs using datasets that may contain minor errors resulting from limitations in map construction methodologies.

CoGe (http://genomevolution.org/CoGe/SynFind.pl )

Satsuma (http://evomics.org/learning/genomics/satsuma/)

Cinteny (http://cinteny.cchmc.org/) ,

Cinteny server can be used for finding regions syntenic across multiple genomes and measuring the extent of genome rearrangement using reversal distance as a measure.

OrthoCluster (http://krono.act.uji.es/noticias/orthocluster-a-new-tool-for-mining-syntenic-blocks)

A new tool for mining syntenic blocks in comparative genomics

SynMap (http://genomevolution.org/wiki/index.php/SynMap),

SyMAP (http://www.symapdb.org/)

SyMAP (Synteny Mapping and Analysis Program) v4.0 is an automated system for identifying and displaying genome synteny alignments. The genomes may be represented by sequenced chromosomes (pseudomolecules), by draft sequence contigs, or by FPC physical maps (with BAC-end or marker sequence).

http://genomevolution.org/CoGe/SynMap.pl

RegionMiner (http://www.genomatix.de/online_help/help_regionminer/orthologous.html)

SyntenyMiner is being developed as an application to visualize and interrogate comparisons among multiple complete genome sequences. http://syntenyminer.sourceforge.net/

AutoGRAPH ( http://autograph.genouest.org/),

AutoGRAPH is an integrated web server for multi-species comparative genomic analysis. It is designed for constructing and visualizing synteny maps between two or three species, determination and display of macrosynteny and microsynteny relationships among species, and for highlighting evolutionary breakpoints.

SynChro(http://www.lgm.upmc.fr/CHROnicle/SynChro.html)

SynChro is a tool designed to define conserved synteny blocks. It reconstructs synteny blocks between pairwise comparison of multiple genomes. The reconstructed synteny blocks may overlap each other, be included in one another or duplicated due to micro-rearrangements.

SyntenyView ( http://www.cbs.dtu.dk/dtucourse/cookbooks/nikob/exercises/gf1_output_5.html),

Ensembl 'SyntenyView' shows conservation of large-scale gene order between species pairs. A brief summary of the calculation method appears at the bottom of this help page.  The left of a 'SyntenyView' page displays a diagram of chromosomes with blocks of conserved synteny. The right of a page shows homology matches between individual genes within syntenic blocks.

SynBrowse ( http://www.synbrowse.org/),

SynBrowse (Synteny Browser) is a generic sequence comparison tool for visualizing genome alignments both within and between species. It is intended to help scientists study and analyze synteny, homologous genes and other conserved elements between sequences. This software is useful in studying genome duplication and evolution. It can also aid in identifying uncharacterized genes, putative regulatory elements and novel structural features of study species by comparing to a well annotated reference sequence, thus enabling genome curators to refine and edit annotations of species that have incomplete genome annotations.

Sibelia (http://arxiv.org/abs/1307.7941).

A comparative genomic tool: It assists biologists in analysing the genomic variations that correlate with pathogens, or the genomic changes that help microorganisms adapt in different environments. Sibelia will also be helpful for the evolutionary and genome rearrangement studies for multiple strains of microorganisms.

GSV (http://cas-bioinfo.cas.unt.edu/gsv/homepage.php)

Genome Synteny Viewer allows users to upload files which contain synteny regions between two or more genomes and interactively visualize the synteny between them. GSV also allows users to upload annotation files to visualize annotated regions in addition to synteny regions.

MicroSyn (http://www.lgm.upmc.fr/CHROnicle/SynChro.html)

MicroSyn software as a means of detecting microsynteny in adjacent genomic regions surrounding genes in gene families. MicroSyn searches for conserved, flanking colinear homologous gene pairs between two genomic fragments to determine the relationship between two members in a gene family.

SynOrth (http://synorth.genereg.net/)

Synorth [s n ôrth], named in combination of "synteny" and "ortholog", is designed for the study of evolutionary changes of genomic regulatory blocks (GRBs) in vertebrate genomes, and especially the changes following the whole-genome duplication in teleost fish, by tracing the ortholog genes gain and loss in ancient synteny blocks.

SyDiG (http://www.ncbi.nlm.nih.gov/pubmed/21441096)

Uncovering Synteny in Distant Genomes.

MapSynteny  (http://www.automatizacionysistemas.com/download.html)

MapSynteny is a macro in MS Excel® able to create images to show the relationship between genetic maps and large sequences (scaffolds, chromosomes, BACs, etc.). Based on tab – delimited BLAST results and some formulas, a suitable image of syntenic relationships or physical mapping can be obtained. http://www.automatizacionysistemas.com/Poster_MapSynteny.pdf

One of the best synteny tutorial for beginer @ http://www.nature.com/scitable/topicpage/synteny-inferring-ancestral-genomes-44022

Reference:

http://www.nature.com/scitable/topicpage/synteny-inferring-ancestral-genomes-44022

http://www.nature.com/nature/journal/v491/n7424/full/nature11622.html

http://en.wikipedia.org/wiki/Synteny

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2675965/

The most common newbie questions are:

Should I try to use these free open source programs?  Why are we not trying GUI software for computational analysis? Should I use commercial bioinformatics programs/software?”

1. Let’s be open

We generally think free and cheap are useless. But this concept is not applicable when we discuss open source software. Mostly, the bioinformatics software is developed by highly competitive biological programmers who believe in open sharing of knowledge. They come under Open Bioinformatics Foundation or O|B|F which is a non-profit, volunteer run organization focused on supporting open source programming in bioinformatics. The best part about open source tools/software is that they’re free to download the source code and read exactly what the program does. If you are so inclined, you can view all of the parts of the program and see the logical flow of the pipeline. In addition, open source makes an excellent learning tool for any beginning bioinformatician. Moreover, you can modify existing open source programs to deal with cutting-edge problems or to customize your pipeline. Apart from your computational and analysis work, most of the reviewer also prefers the open source based results so that they can validate the results if validation required.

2. Code headache

As a bioinformatician you are supposed to know the basics of programming languages, and if you are not good at it, then please learn it as soon as possible because you are not a bio-analyst but biological programmers. The open source programs usually lack dedicated service and support teams (often because they were the product of an overworked doc/postdoc!) so you are responsible for troubleshooting your own errors most of the time. We commonly receive the HELP email to support and assist to setup the pipeline; you can also find this kind of request on any QA forum. I personally believe this coding horror brings the biggest downside of open-source programs; where you need some programming skills in order to implement the program in your pipeline. But, if you are not able to fix the pipeline and modify the open source code according to your requirements them you should re-think on your bioinformatician name tag!!!

3. Dive into the codes

Some of the biologist turn bioinformatician says “if you can do the same thing with commercial software then why to get migraine with weird codes”, well this statement looks to me that guys are keen to learn swimming but still don’t like to get wet. If you are still using paid software and doing your work by customer support and clicking some of the well-designed GUI button then perhaps you are not interested in learning and trying new and challenging bioinformatics works. You are missing the basic flavour of bioinformatics. Let’s dive into the coding world, I am sure your will enjoy it. I recommend your to swim freely in code’s sea, and enjoy the journey; do not merely watch it from the outside.  

4. Paid does not mean better

The bioinformatics company which are specializes in bioinformatics solutions develop well designed/packed, user friendly software by using a large number of specialised scientist, programmers and support staff. They also provide good services to accomplice your biological analysis work. This means that if you hit a ‘snag’ with your data, help is likely only a phone call away! These companies price their products competitively against the cost of a dedicated bioinformatician. You may be able to afford the program, but not the additional staff! Additionally, most of the functionality that you need in your analysis is already coded into the program. Need to plot a graph? Just click this button right here. It is that easy. But, as a bioinformatician this is not generally well encouraged approach in biological analysis work, because the software is not available to everyone and your data can’t be validated. Moreover, there is very less chances that anyone will repeat your work or love to do similar kind of research (because not all the labs in the world are rich like yours).

5. Take a caution

In biological analysis work, in which you deal GB/TB of data are having maximum chances of getting errors, so please be careful and always cross check your data before coming to any conclusion. Even an error in two line code can alter your entire analysis and display weird results. Some of the scientist blindly believes on commercial software, which is entirely wrong. Using proprietary tools does not absolve you of the need to actually read and research the type of analysis that you are doing. This is particularly true in the case of genome assembly and annotation.

At the end, I would like to tell only one think that open source solutions allows you to do more cutting edge analysis than the commercial tools. So let’s go for it.

Disclaimer:

This is my personal view. I have nothing to do with any company or open source community. The views expressed on these pages are mine alone and not those of my current/past employers. I do reserve the right to remove comments left by spammers or off-topic comments.

Today, refreshing my old memories I decided to blog about the basic knowledge of biochemistry and computational proteomics skills, but after I found several article on internet saying exactly what I had wanted to say I thought I might as well just redirect BOL's blog readers there instead:

Here is the list of website and videos links which provide a good resource for you basic chemistry need:

http://tecreativ.blogspot.co.uk/2012/09/funny-shortcut-remember-periodic-table.html

This blog have some specific hindi word to remember entire periodic table. I really like

Group 14 (C Si Ge Sn Pb) -> Sentence “Chemistry Sir Gives Sanki Problems”

Sanki is a hindi word which mean crazy :P

I found this link useful as well http://www.wikihow.com/Memorise-the-Periodic-Table

The eagle genomics group provide an element of bioinformatics in periodic tables. Yes you got it, this is not periodic table rather bioinformatics tools with periodicals

http://elements.eaglegenomics.com/

You can also try this video links, which provide you an overview with tricks on periodic tables:

http://www.youtube.com/watch?v=fLSfgNxoVGk

http://www.youtube.com/user/periodicvideos

For drug design educational material, software, tools, databses, viewer, file format and many more stuff at one place http://www.allfordrugs.com/drug-design/ I highly recommend you all computational drug designer to bookmark this page for future studies as well.

I just remember one of my mini project in which I use my flash knowledge (flash .. oh ya flash) to explain amino acids in interactive and user friendly manner. I can’t provide It right now, but promise you to provide a link in near future. I hope that you will enjoy my flashy creative skills :).

Moreover, I found some of very interesting tricks to remember all amino acids chemical formulae on youtube at

http://www.youtube.com/watch?v=gqrWb0fmzQ&list=PL6132651E70BB5575

http://www.youtube.com/watch?v=C2GfoGXfySQ&list=PL6132651E70BB5575

Key points for computer added drug designers?
1. A shortage of biochemistry skills means that you absolutely nowhere in understanding the key concept and do research.
2. Keep handy with complex mathematical formula, before merely running tools or software.
3. Dig it better and deeper guys .. design it.